Search

In complex mountainous areas where earthquakes are frequent, landslide hazards pose a significant threat to human life and property due to their high degree of concealment, complex development mechanism, and abrupt nature. In view of the problems of the existing landslide hazard susceptibility evaluation model, such as poor effectiveness and inaccuracy of landslide hazard data and the need for experts to participate in the calculation of a large number of evaluation factor weight classification statistics. In this paper, a combined SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and PSO-RF (Particle Swarm Optimization-Random Forest) algorithm was proposed to evaluate the susceptibility of landslide hazards in complex mountainous regions characterized by frequent earthquakes, deep river valleys, and large terrain height differences. First, the SBAS-InSAR technique was used to invert the surface deformation rates of the study area and identified potential landslide hazards. Second, the study area was divided into 412,585 grid cells, and the 16 selected environmental factors were analyzed comprehensively to identify the most effective evaluation factors. Last, 2722 landslide (1361 grid cells) and non-landslide (1361 grid cells) grid cells in the study area were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide and non-landslide data, the performances of the PSO-RF algorithm and three other machine learning algorithms, BP (back propagation), SVM (support vector machines), and RF (random forest) algorithms were compared. The results showed that 329 potential landslide hazards were updated using the surface deformation rates and existing landslide cataloguing data. Furthermore, the area under the curve (AUC) value and the accuracy (ACC) of the PSO-RF algorithm were 0.9567 and 0.8874, which were higher than those of the BP (0.8823 and 0.8274), SVM (0.8910 and 0.8311), and RF (0.9293 and 0.8531), respectively. In conclusion, the method put forth in this paper can be effectively updated landslide data sources and implemented a susceptibility prediction assessment of landslide disasters in intricate mountainous areas. The findings can serve as a strong reference for the prevention of landslide hazards and decision-making mitigation by government departments. [ABSTRACT FROM AUTHOR]

Introduction: Leaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider. Methods: Taking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples. Results: The ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R² (R² = 0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54) Discussion: This study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing. [ABSTRACT FROM AUTHOR]

In recent years, the new agricultural business entities have grown rapidly in all parts of the country and have become an effective carrier to achieve the basic stability of China's rural family operation system and moderate-scale agricultural operation. However, the systemic defects of rural financial market and the credit rationing of formal financial institutions make the capital needs of rural areas unsatisfied for a long time. Financial demand determines the direction of supply reform. Therefore, in order to improve rural financial supply and promote the transformation and development of agricultural economy under the current situation, it is necessary to effectively understand the formal credit demand and the impact factors of accessibility of the new agricultural business entities, which represents the direction of China's agricultural development in the future. Based on back propagation (BP) neural network, this paper constructs the farmers' formal credit availability prediction model and studies the formal credit demand and the impact factors of the availability of new agricultural business entities. The experimental results show that the farmers' formal credit availability prediction model has good performance in prediction accuracy, prediction time, and mean squared error. [ABSTRACT FROM AUTHOR]

The factors influencing precipitation in western China are quite complex, which increases the difficulty in determining accurate predictors. Hence, this paper models the monthly measured precipitation data from 240 meteorological stations in mainland China and the precipitation data from the European Centre for Medium-Range Weather Forecasts and the National Climate Centre and employs 88 atmospheric circulation indices to develop a precipitation prediction scheme. Specifically, a high-quality grid-point field is created by fusing and revising the precipitation data from multiple sources. This field is combined with the Empirical Orthogonal Function decomposition and the causal information flow. Next, the best predictors are screened through Empirical Orthogonal Function decomposition and causal information flow, and a data-driven precipitation prediction model is established using a Back Propagation Neural Network and a Random Forest algorithm to conduct the 1-month, 3-month, and 6-month precipitation predictions. The results show that: The machine learning-based precipitation prediction model has high accuracy and is generally able to predict the precipitation trend in the western region better. The Random Forest algorithm significantly outperforms the Back Propagation Neural Network algorithm in the prediction of the three starting times, and the prediction ability of both models gradually decreases as the starting time increases. Compared with the 2022 flood season prediction scores of the Institute of Atmospheric Sciences of the Chinese Academy of Sciences, the model improves the prediction of 1-month and 3-month precipitation in the western region and provides a new idea for the short-term climate prediction of precipitation in western China. [ABSTRACT FROM AUTHOR]

The mechanical performance analysis of the members is the primary basis for evaluating the hoisting quality and safety of the valve hall grid structure. Ordinarily, manual analysis of monitoring data and on-site experience inspection are employed to structural judgment, but it is challenging to evaluate the correlation of the various members and the overall safety of a valve hall. In this paper, an intelligent correlation real-time analysis method based on a BPNN (Back Propagation Neural Network) for the mechanical properties of members is proposed to intelligently control the safety of valve hall grid structure hoisting. The correlation between the mechanical properties of multi-points in the grid structure is used to model the target measuring points. In addition, an intelligent real-time analysis system is used to manage and apply the mechanical property correlation and abnormality of members in real-time. Then, the model is applied to a super-span valve hall in South China, and the application effect is good. The mechanical property correlation model can accurately reflect the mechanical state of the valve hall grid structure hoisting process. Simultaneously, it can effectively pinpoint hidden dangers and locate risk members. It provides a new reference for the normal operation and maintenance of a super-span valve hall grid. [ABSTRACT FROM AUTHOR]

The Huanglong Scenic and Historic Interest Area in China, a UNESCO World Heritage Site, is famous for its large-scale, diverse, intricately structured, and brightly colored surface travertine landscapes. However, severe degradation of the Huanglong travertine formations, such as blackening and algae erosion, has occurred in recent years, necessitating monitoring and identification. We collected hyperspectral reflectance data of the travertine formations in different states and bare ground using a ground-based hyperspectral radiometer (PSR-2500) from ASD company. After conducting a correlation analysis between the hyperspectral reflectance data and the travertine formations, we identified healthy travertine formations, blackened travertine formations, travertine formations affected by algae erosion, and bare ground. The Siamese network method was employed to generate data labels, and the spectral features of the travertine formations were extracted by combining the sensitive bands with pre-processed and reduced data. The PSO-BPNN classifier was developed by optimizing the back propagation neural network (BPNN) using the particle swarm optimization algorithm (PSO). To verify the effectiveness of PSOBPNN in accurately distinguishing different states of travertine formations, we compared its performance with that of BPNN using three performance indices. Finally, the proposed method was applied to the real-world hyperspectral image data collected by the Micro-Hyperspectral imaging instrument to classify the travertine formations in different states and bare ground. The test set demonstrated good overall performance, with an average overall accuracy (OA) of 0.93, F1-score of 0.92, and Kappa coefficient of 0.97. [ABSTRACT FROM AUTHOR]

With the application of machine learning rapidly gaining popularity in computer science and other fields, neural network techniques have successfully simulated the performance of in-service pavements as they are efficient in predicting and solving nonlinear relationships and in dealing with uncertain large-area pavement problems. In this paper, we address the problem of the optimal timing of preventive maintenance of asphalt pavements to accurately predict the condition index (pavement condition index, PCI) of highway asphalt pavements and develop a highly accurate, long-period, multifactor prediction model with the suitability of preventive maintenance at its core. The prediction model is called differential evolution particle swarm optimization back propagation (DEPSO-BP) neural network, and the input dimension of the prediction model is determined by gray correlation analysis (GCA), and DEPSO is used to improve the search efficiency of BP neural network and the asphalt pavement usage performance with parameter continuity prediction model. Finally, the Qinglan Highway (G22) PCI of Gansu Province, China, is selected for example validation, and the prediction results are compared with those of the four models. The results show that the multifactor prediction model based on DEPSO-BP neural network has good generalization ability. This model is important for improving the economic efficiency of road maintenance, and can be used in the long-cycle process to provide model reference and scientific basis for the subsequent road maintenance budget application and decision-making scheme. [ABSTRACT FROM AUTHOR]

Using the Gravity Recovery and Climate Experiment (GRACE) satellite to monitor groundwater storage (GWS) anomalies (GWSAs) at the local scale is difficult due to the low spatial resolution of GRACE. Many attempts have been made to downscale GRACE-based GWSAs to a finer resolution using statistical downscaling approaches. However, the time-lag effect of GWSAs relative to environmental variables and optimal model parameters is always ignored, making it challenging to achieve good spatial downscaling, especially for arid regions with longer groundwater infiltration paths. In this paper, we present a novel spatial downscaling method for constructing GRACE-based 1 km-resolution GWSAs by using the back propagation neural network (BPNN) and considering the time-lag effect and the number of hidden neurons in the model. The method was validated in Alxa League, China. The results show that a good simulation performance was achieved by adopting varying lag times (from 0 to 4 months) for the environmental variables and 14 hidden neurons for all the networks, with a mean correlation coefficient (CC) of 0.81 and a mean root-mean-square error (RMSE) of 0.70 cm for each month from April 2002 to December 2020. The downscaled GWSAs were highly consistent with the original data in terms of long-term temporal variations (the decline rate of the GWSAs was about −0.40 ± 0.01 cm/year) and spatial distribution. This study provides a feasible approach for downscaling GRACE data to 1 km resolution in arid regions, thereby assisting with the sustainable management and conservation of groundwater resources at different scales. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) theory is a rapidly developing information science around the world. The Back Propagation (BP) network is one of many artificial neural network types. It is a multilayer feedforward network with sophisticated nonlinear mapping capabilities. It is actually a relatively complex and nonlinear comprehensive decision-making problem when taking into account the comprehensive evaluation of test scores based on evaluation indicators that reflect the quality of the test papers. The five major factors of education service quality are tentatively proposed in this paper, which uses a neural network to model and analyze it. This paper examines the five factors of the education service quality evaluation model one by one and analyzes the issues that exist in the education service quality of international students in China. According to empirical research, overall satisfaction with the quality of education service in Guangxi among foreign students in China is low, and subindicators of education service quality are unbalanced. This paper analyzes the reasons for this situation from the three levels of service consciousness, emotional investment, and professional quality and proposes that the main task of improving education service quality is to optimize the entire service process, in order to narrow the gap between foreign students' expectations and actual perceptions of educational services. [ABSTRACT FROM AUTHOR]

With the continuous improvement of people's living standards, the travel demand for vehicles increases rapidly during weekends and holidays. This situation leads to a number of negative impacts on the transport network, such as traffic congestion, carbon emissions, and long queues at refueling stations. The impact of vehicles requiring energy replenishment on the negative effect is nonlinear. Therefore, the negative effect data of working days cannot be directly used to evaluate the cost, and the traffic equilibrium allocation problem also needs to be solved. An equilibrium allocation model of hybrid vehicles considering the energy replenishment demand in holidays is established, aiming at minimizing the total time cost and energy consumption cost. The Frank–Wolfe algorithm is used to solve the traffic allocation problem in advance, and the negative effect of the urban energy network during holidays is predicted by the Genetic Algorithm—Back Propagation (GA-BP) algorithm. Then, the energy-replenishing vehicles in the traffic network are induced to reduce the total cost. Finally, taking the actual road network in Jiulongpo District of Chongqing City, southwest of China, as an example, the negative effects of multiple stations are predicted. The results show that the prediction method proposed in this paper is effective during holiday periods. In addition, as the market share of electric vehicles increases, the negative cost can decrease gradually. The predicted results can provide a reference for traffic managers. [ABSTRACT FROM AUTHOR]

As China's distributed energy is still in the development stage, energy transmission loss will inevitably occur in the transmission process from the source end to the load end. To reduce transmission energy loss, we should also beware of electricity theft. The principle of common electricity theft methods is analyzed to improve the accuracy of established electricity theft characteristics and electricity theft detection. The ReliefF multivariate characteristics selection algorithm optimizes the electricity theft characteristics. The back propagation (BP) neural network-based electricity theft detection model is built, and the optimized characteristics are selected as the model's input. The experiment results show that the detection model has better electricity theft identification accuracy using the optimized characteristics for electricity theft detection. [ABSTRACT FROM AUTHOR]

In 2022, as a result of the historically exceptional high temperatures that have been observed this summer in several parts of China, particularly in the province of Sichuan, residential demand for energy has increased. Up to 70% of Sichuan's electricity comes from hydropower, thus creating a sensible and practical reservoir scheduling plan is essential to maximizing reservoir power generating efficiency. However, classical optimization, such as back propagation (BP) neural network, does not take into account the correlation of samples in time while generating reservoir scheduling rules. We proposed a prediction model based on LSTM neural network coupled with wavelet transformation (WT-LSTM) to address the problem. In order to extract the reservoir scheduling rules, this paper first gathers the scheduling operation data from the Xiluodu hydropower station and creates a dataset. Next, it uses the feature of the time-series prediction model with the realization of a complex nonlinear mapping function, time-series learning capability, and high prediction accuracy. The results demonstrate that the time-series deep learning network has high learning capability for reservoir scheduling by comparing evaluation indexes such as root mean square error (RMSE), rank-sum ratio (RSR), and Nash–Sutcliffe efficiency (NSE). The WT-LSTM network model put forward in this research offers better prediction accuracy than conventional recurrent neural networks and serves as a reference base for scheduling decisions by learning previous scheduling data to produce outflow solutions, which has some theoretical and practical benefits. [ABSTRACT FROM AUTHOR]

Pipeline transportation of crude oil has great advantages over traditional oil transmission methods, in terms of economic and environmental protection. The main costs in the oilfield surface system are the fuel costs for heating the crude oil during transportation and the electricity costs for the pumping units. In the northeast of China, where winter temperatures are extremely low and the oil has a high freezing point and high viscosity, higher temperatures, and pressures are required to transport crude oil. With machine learning widely used in many industries and achieving better results, the digital management of oil pipelines has stored a large amount of production and operation data, which has laid the foundation for the research of oil pipeline process calculation using machine learning methods. In this paper, a crude oil pressure drop calculation of an oil pipeline in Northeast China is carried out based on a neural network. For pipeline pressure drop calculation, the back propagation neural network (BP) pressure drop calculation model and particle swarm optimization for back propagation neuron network (PSO-BP) pressure drop calculation model are established. Two models were used to calculate and compare the measured data, and the average absolute error of the PSO-BP model was the smallest, which was 0.015%. Compared with the BP model, the average relative error is reduced by 13.16%. Therefore, The PSO-BP pressure drop calculation model has high accuracy and is of practical significance for predicting pipeline pressure drop. [ABSTRACT FROM AUTHOR]

Grain size is the basic property of intertidal zone sediment. Grain size acts as an indicator of sedimentary processes and geomorphological evolution under human and nature interactions. The remote sensing technique provides an alternative for sediment grain-size parameter monitoring with the advantages of wide coverage and real-time surveying. This paper attempted to map the distributions of three sediment grain size contents and the mean grain size with multitemporal Landsat images along the southwestern coast of Laizhou Bay, China, from 1989 to 2015. Considering the low correlations between the measured reflectance and grain-size parameters, we used a support vector machine (SVM) to develop a nonlinear calibration model by taking several band indices as input variables. Then, the performance of the back propagation neural network (BPNN) was determined and discussed with that of the SVM. The SVM performed better than the BPNN in calibrating the four grain-size parameters based on a comparison of R2 and the root-mean-square error (RMSE). Moreover, an atmospheric correction algorithm originally proposed for case II water enabled the TM\ETM+ images to be precisely atmospherically corrected in this study. The SVM-mapped spatial-temporal grain-size variation showed a coarsening trend, which agreed with that obtained during in situ measurements in a former study. The changes in Yellow River discharge and precipitation associated with the coarsening trend were further analyzed. The yielded results showed that the coarsening trend and reduction in tidal flat area might be aggravated with overutilization. More reasonable planning would be necessary in this case. [ABSTRACT FROM AUTHOR]

With the development of China's economy, more and more energy consumption has led to serious environmental problems. Faced with the enormous pressure of large amounts of carbon dioxide ( C O 2 ) emissions, China is now actively implementing the development strategy of low-carbon and emission reduction. Through the analysis of the influencing factors of C O 2 emissions in China, five key influencing factors are selected: urbanization level, gross domestic product (GDP) of secondary industry, thermal power generation, real GDP per capital and energy consumption per unit of GDP. This paper applies the Elman neural network optimized by the Firefly Algorithm (FA) to forecast the C O 2 emissions in China. And the results show that the performance of the FA–Elman is better than the Elman neural network and Back Propagation Neural Network (BPNN), verifying the effectiveness of the FA–Elman model for the C O 2 emissions prediction. Finally, we make some suggestions for low-carbon and emission reduction in China by analysing key influencing factors and forecasting C O 2 emissions using the FA–Elman model from 2017 to 2020. [ABSTRACT FROM AUTHOR]

The demand analysis method of maritime emergency resources is the key technology to promote a reasonable emergency resource allocation during maritime emergency management. It is widely used to improve the efficiency of maritime emergency rescue and reduce the loss of maritime accidents. However, it requires a scientific and effective method of the demand analysis of maritime emergency resources. This paper aims to analyze the underlying modeling paradigms and to assess the extent to which the demand analysis methods of maritime emergency resources can meet the requirements. Focusing on the demand analysis methods, this paper provides a broad overview of the current literature on maritime emergency resources of the last decades, by considering the models' purposes, theoretical frameworks, factors, and outputs. The results indicate that the existing methods can be classified into three concepts: the linear regression theory, Back Propagation (BP) Neural Network, and Case-based Reasoning (CBR) technology. Combined with the characteristics of China's maritime emergency management field, the interaction between theoretical framework and applications is not sufficiently understood and thus needs to be further studied. Being familiar with knowledge gaps acts as a catalyst for further research on scientific and efficient demand analysis methods of maritime emergency resources in various navigation conditions. [ABSTRACT FROM AUTHOR]

A quasi-continuous high magnetic field is a special kind of pulsed magnetic field with a flat-top field longer than 100 ms. This paper proposes a hybrid power supply that consists of a capacitor power supply and a generator-rectifier power supply. A dual-coil magnet is powered to generate a 65 T/100 ms quasicontinuous high magnetic field with a ripple less than 500 ppm at the Wuhan National High Magnetic Field Center, Wuhan, China. A protection circuit consists of an R-C branch and a diode array is discussed for the worst short-circuit fault, which occurs between the first turns of inner coil and outer coil. This type of faultwill produce oscillation voltage with a high peak value and high frequency on the rectifiers. A self-adaptive PI controller with the coefficients corrected by a back propagation neural network is adopted to reduce the ripple of a flat-top. [ABSTRACT FROM AUTHOR]

This paper presents the assessment of water resource security in the Guizhou karst area, China. A mean impact value and back-propagation (MIV-BP) neural network was used to understand the influencing factors. Thirty-one indices involving five aspects, the water quality subsystem, water quantity subsystem, engineering water shortage subsystem, water resource vulnerability subsystem, and water resource carrying capacity subsystem, were selected to establish an evaluation index of water resource security. In addition, a genetic algorithm and back-propagation (GA-BP) neural network was constructed to assess the water resource security of Guizhou Province from 2001 to 2015. The results show that water resource security in Guizhou was at a moderate warning level from 2001 to 2006 and a critical safety level from 2007 to 2015, except in 2011 when a moderate warning level was reached. For protection and management of water resources in a karst area, the modes of development and utilization of water resources must be thoroughly understood, along with the impact of engineering water shortage. These results are a meaningful contribution to regional ecological restoration and socio-economic development and can promote better practices for future planning. [ABSTRACT FROM AUTHOR]

Grain quality changes during the storage period, and an important grain quality indictor is the free fatty acid (FFA) content. Understanding real-time change of FFA content in stored grain is significant for grain storage safety. However, the FFA content requires manual detection with time-consuming and complex procedures. Thus, this paper is dedicated to developing a method to estimate FFA content in stored grain accurately. We proposed a machine learning approach—multiple-kernel support vector regression—to complete this goal, which improved the accuracy and robustness of the FFA estimation. The effectiveness of the proposed approach was validated by the grain storage data collected from northeast China. To show the merits of the proposed method, several prevailing prediction methods, such as single-kernel support vector regression, multiple linear regression, and back propagation neural network, were introduced for comparative purposes, and several quantitative statistical indexes were adopted to evaluate the performance of different models. The results showed that the proposed approach can achieve a high accuracy with mean absolute error of 0.341 mg KOH/100 g, root mean square error of 0.442 mg KOH/100 g, and mean absolute percentage error of 2.026%. Among the four models tested, the multiple-kernel support vector regression model performed best and made the most robust forecasts of FFA content in stored grain. [ABSTRACT FROM AUTHOR]

Statistical model is a traditional safety diagnostic model for dam seepage. It can hardly display the nonlinear relationship between dam seepage and the load sets and has the disadvantage of poor extension prediction. In this paper, the theories of Back Propagation Neural Network (BPNN) combined with Genetic Algorithm (GA) are applied to the seepage prediction model. Taking a typical dam in China as an example, the prediction results of BPNN-GA model and statistical model are compared with the monitoring values. The results show that the improved dam seepage model enhances the ability of nonlinear mapping and generalization and makes the seepage prediction more accurate and reasonable in the near future. According to the established criterion, the safety state of the dam in flood season is evaluated. [ABSTRACT FROM AUTHOR]

Natural rubber is mainly dependent on import in China, its domestic market price is influenced by the Natural Rubber Customs Declaration Price (NRCDP). Considering the fluctuating properties of the NRCDP, a method of the NRCDP based on Wavelet and the optimized Back Propagation (BP) neural network Group using a Genetic Algorithm (W-GA-BPG) is proposed. First, an NRCDP dataset is established based on the original Customs Declaration Price (CDP) dataset collected by Qingdao Customs, in which the commodity types are selected consistently according to the sampling intervals, and the features are deleted if they are less affected by the fluctuation of NRCDP. Secondly, the selected features in NRCDP are decomposed using wavelet transform to obtain a group of feature sequences with different scales. Then, a Group of BP neural networks (BPG) optimized by Genetic Algorithm (GA) is used to predict multiple decomposition sub-sequences, respectively. Finally, the predicted values are obtained through wavelet reconstruction. Combined with the NRCDP dataset, the W-GA-BPG model is established by comparing and analyzing experiments by evaluating the Mean Square Error (MSE) and determination coefficient of the prediction results. The MSE and determination coefficient predicted using the proposed model are 0.0043 and 0.9302, respectively, which is the best prediction effect. [ABSTRACT FROM AUTHOR]

Landslide disasters are one of the main risks involved with the operation of long-distance oil and gas pipelines. Because previously established disaster risk models are too subjective, this paper presents a quantitative model for regional risk assessment through an analysis of the patterns of historical landslide disasters along oil and gas pipelines. Using the Guangyuan section of the Lanzhou–Chengdu–Chongqing (LCC) long-distance multiproduct oil pipeline (82 km) in China as a case study, we successively carried out two independent assessments: a susceptibility assessment and a vulnerability assessment. We used an entropy weight method to establish a system for the vulnerability assessment, whereas a Levenberg–Marquardt back propagation (LM-BP) neural network model was used to conduct the susceptibility assessment. The risk assessment was carried out on the basis of two assessments. The first, the system of the vulnerability assessment, considered the pipeline position and the angle between the pipe and the landslide (pipeline laying environmental factors). We also used an interpolation theory to generate the standard sample matrix of the LM-BP neural network. Accordingly, a landslide susceptibility risk zoning map was obtained based on susceptibility and vulnerability assessment. The results show that about 70 % of the slopes were in high-susceptibility areas with a comparatively high landslide possibility and that the southern section of the oil pipeline in the study area was in danger. These results can be used as a guide for preventing and reducing regional hazards, establishing safe routes for both existing and new pipelines, and safely operating pipelines in the Guangyuan area and other segments of the LCC oil pipeline. [ABSTRACT FROM AUTHOR]

The UN 2030 Agenda sets poverty eradication as the primary goal of sustainable development. An accurate measurement of poverty is a critical input to the quality and efficiency of poverty alleviation in rural areas. However, poverty, as a geographical phenomenon, inevitably has a spatial correlation. Neglecting the spatial correlation between areas in poverty measurements will hamper efforts to improve the accuracy of poverty identification and to design policies in truly poor areas. To capture this spatial correlation, this paper proposes a new poverty measurement model based on a neural network, namely, the spatial vector deep neural network (SVDNN), which combines the spatial vector neural network model (SVNN) and the deep neural network (DNN). The SVNN was applied to measure spatial correlation, while the DNN used the SVNN output vector and explanatory variables dataset to measure the multidimensional poverty index (MPI). To determine the optimal spatial correlation structure of SVDNN, this paper compares the model performance of the spatial distance matrix, spatial adjacent matrix and spatial weighted adjacent matrix, selecting the optimal performing spatial distance matrix as the input data set of SVNN. Then, the SVDNN model was used for the MPI measurement of the Yangtze River Economic Belt, after which the results were compared with three baseline models of DNN, the back propagation neural network (BPNN), and artificial neural network (ANN). Experiments demonstrate that the SVDNN model can obtain spatial correlation from the spatial distance dataset between counties and its poverty identification accuracy is better than other baseline models. The spatio-temporal characteristics of MPI measured by SVDNN were also highly consistent with the distribution of urban aggregations and national-level poverty counties in the Yangtze River Economic Belt. The SVDNN model proposed in this paper could effectively improve the accuracy of poverty identification, thus reducing the misallocation of resources in tracking and targeting poverty in developing countries. [ABSTRACT FROM AUTHOR]

Accurate heat load prediction is the key to achieve fine control, energy conservation, and carbon reduction of regional hydronics. Taking the regional hydronics of a city in the north of China as the research object, the author, respectively uses back propagation neural network (BPNN), genetic algorithm (GA) optimized BPNN (GA-BPNN), and autoregressive integrated moving average model (ARIMA) combined BPNN (ARIMA BPNN) to predict its heat load, and compares the accuracy and applicability of each prediction method. The results indicate that GA-BPNN has the smallest prediction error, followed by ARIMA-BPNN, but the latter requires less data for prediction. In practical engineering, if there is a sufficient amount of data related to heat load, it is recommended to use GA-BPNN. If there is a small amount of data, ARIMA-BP prediction method can be used. [ABSTRACT FROM AUTHOR]

As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important practical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algorithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary decomposition, the use of secondary decomposition, and the innovative combination of three optimal models to forecast carbon price, but it still needs to be optimized for practice. [Display omitted] • Propose improve CEEMDAN by using Hermite interpolation and calculating residuals first. • High complexity components are secondarily decomposed to reduce its complexity. • Improved RF by SSA, named SSARF, is proposed to solve the dilemmas of RF. • Propose a high-accuracy hybrid model for carbon price forecasting. • Compare multi-faceted experiments to prove the validity of the proposed model. [ABSTRACT FROM AUTHOR]

When flying through clouds that co ntain supercooled liquid water, aircraft can accumulate ice at levels that can be dangerous to flight. Similarly, in the field of weather modification, the amount and distribution of a cloud's supercooled water content is an important indicator of artificial precipitation. The G-band water Vapor Radiometer (GVR) is a small instrument that can be mounted on aircraft. Its operating frequency is highly sensitive to low liquid water content, which is particularly suitable for detecting supercooled water in different parts of clouds during changes in flight position. In order to explore supercooled water, we installed such an instrument with four double-sideband receiver channels centered at 183.31 ± 1, ±3, ±7, and ±14GHz, under the right wing of a King Air B-10KL that we used to perform a series of outfield observations and artificial precipitation missions. In this paper, we discuss the feasibility of detecting supercooled liquid water in the clouds over North China with a GVR. A Back Propagation (BP) neural network algorithm that takes into account flight details including aircraft altitude, climate background and geographic location variations was developed to retrieve supercooled liquid water path (SLWP). A numerical simulation test using the radiosonde data as test samples showed that the retrieval results correlate highly with the radiosonde data calculation results, and that the algorithm was applicable for different seasons and different geographical locations. The algorithm was then applied to the aircraft outfield observations of supercooled liquid water and the results were compared with hotwire, another cloud liquid water observation instrument installed on the aircraft. Results here showed agreement in terms of trend. The observation of SLWPs over clouds thought to be of 0-value shows that the root mean square error of retrieval results is as low as 0.2 g m−2. This paper demonstrates that it is feasible to measure supercooled water over North China with GVR using the retrieval algorithm used here, which obtained good results and is able to compensate for the lack of reliable supercooled water detection instruments on the aircraft. • The feasibility of detecting supercooled liquid water in the clouds over North China with a GVR is discussed. • A Back Propagation(BP) neural network algorithm was developed to retrieve supercooled liquid water path(SLWP). • The numerical simulation test showed that the algorithm is applicable for different seasons and varied geographic locations. • The comparison of observations reveals that GVR is more reliable for the detection of supercooled water. [ABSTRACT FROM AUTHOR]

Soil conservation planning often requires estimates of the spatial distribution of soil erosion at a catchment or regional scale. This paper applied the Revised Universal Soil Loss Equation (RUSLE) to investigate the spatial distribution of annual soil loss over the upper basin of Miyun reservoir in China. Among the soil erosion factors, which are rainfall erosivity ( R), soil erodibility ( K), slope length ( L), slope steepness ( S), vegetation cover ( C), and support practice factor ( P), the vegetative cover or C factor, which represents the effects of vegetation canopy and ground covers in reducing soil loss, has been one of the most difficult to estimate over broad geographic areas. In this paper, the C factor was estimated based on back propagation neural network and the results were compared with the values measured in the field. The correlation coefficient ( r) obtained was 0.929. Then the C factor and the other factors were used as the input to RUSLE model. By integrating the six factor maps in geographical information system (GIS) through pixel-based computing, the spatial distribution of soil loss over the upper basin of Miyun reservoir was obtained. The results showed that the annual average soil loss for the upper basin of Miyun reservoir was 9.86 t ha ya in 2005, and the area of 46.61 km (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.9% very low, 21.89% low, 6.18% moderate, 2.89% severe, and 1.84% very severe. Thus, by using RUSLE in a GIS environment, the spatial distribution of water erosion can be obtained and the regions which susceptible to water erosion and need immediate soil conservation planning and application over the upper watershed of Miyun reservoir in China can be identified. [ABSTRACT FROM AUTHOR]

In order to estimate the energy consumption demand of residential buildings, this paper first discusses the status and shortcomings of current domestic energy consumption models. Then it proposes and develops a residential building energy consumption demand model based on a back propagation (BP) neural network model. After that, taking residential buildings in Chongqing (P.R. China) as an example, 16 energy consumption indicators are introduced as characteristics of the residential buildings in Chongqing. The index system of the BP neutral network prediction model is established and the multi-factorial BP neural network prediction model of Chongqing residential building energy consumption is developed using the Cshap language, based on the SQL server 2005 platform. The results obtained by applying the model in Chongqing are in good agreement with actual ones. In addition, the model provides corresponding approximate data by taking into account the potential energy structure adjustments and relevant energy policy regulations. [ABSTRACT FROM AUTHOR]

Developing a method for quickly estimating the cumulative infiltration in shallow slopes is essential for engineers to evaluate slope stability. In this paper, the hydraulic parameters of the Yan'an loess in China were obtained through physical mechanics test. Numerical experiments with different slope inclinations, soil thicknesses, rainfall intensities, and initial moisture contents were carried out to obtain the cumulative infiltration curves. The Philip model was used to fit the cumulative infiltration curves. The slope inclination, soil thickness, rainfall intensity, and initial moisture content were served as the input variables, and the parameters of the Philip model were the output variables. A generalized regression neural network–based method was proposed to estimate the cumulative infiltration quickly. Three indicators (coefficient of determination, mean squared error, and mean absolute error) show that the performance of the proposed method is superior to those of the multiple linear regression model, the back propagation neural network, and radial basis function neural network. Parametric analysis indicates that rainfall intensity is the most sensitive parameter affecting cumulative infiltration. [ABSTRACT FROM AUTHOR]

Against the backdrop of increasingly serious global climate change and the development of the low-carbon economy, the coordination between energy consumption carbon emissions (ECCE) and regional population, resources, environment, economy and society has become an important subject. In this paper, the research focuses on the security early warning of ECCE in Hebei Province, China. First, an assessment index system of the security early warning of ECCE is constructed based on the pressure-state-response (P-S-R) model. Then, the variance method and linearity weighted method are used to calculate the security early warning index of ECCE. From the two dimensions of time series and spatial pattern, the security early warning conditions of ECCE are analyzed in depth. Finally, with the assessment analysis of the data from 2000 to 2014, the prediction of the security early warning of carbon emissions from 2015 to 2020 is given, using a back propagation neural network based on a kidney-inspired algorithm (KA-BPNN) model. The results indicate that: (1) from 2000 to 2014, the security comprehensive index of ECCE demonstrates a fluctuating upward trend in general and the trend of the alarm level is "Severe warning"-"Moderate warning"-"Slight warning"; (2) there is a big spatial difference in the security of ECCE, with relatively high-security alarm level in the north while it is relatively low in the other areas; (3) the security index shows the trend of continuing improvement from 2015 to 2020, however the security level will remain in the state of "Semi-secure" for a long time and the corresponding alarm is still in the state of "Slight warning", reflecting that the situation is still not optimistic. [ABSTRACT FROM AUTHOR]

Coronavirus disease (Covid-19) caused millions of confirmed cases and deaths worldwide since first appeared in China. Forecasting methods are essential to take precautions early and control the spread of this rapidly expanding pandemic. Therefore, in this research, a new customized hybrid model consisting of Back Propagation-Based Artificial Neural Network (BP-ANN), Correlated Additive Model (CAM) and Auto-Regressive Integrated Moving Average (ARIMA) models were developed for the purpose of forecast Covid-19 prevalence in Brazil, US, Russia and India. The Covid-19 dataset is obtained from the World Health Organization website from 22 January, 2020 to 6 January, 2021. Various parameters were tested to select the best ARIMA models for these countries based on the lowest MAPE values (5.21, 11.42, 1.45, 2.72) for Brazil, the US, Russia and India, respectively. On the other hand, the proposed BP-ANN model itself provided less satisfactory MAPE values. Finally, the developed new customized hybrid model was achieved to obtain the best MAPE results (4.69, 6.4, 0.63, 2.25) for forecasting Covid-19 prevalence in Brazil, the US, Russia and India, respectively. Those results emphasize the validity of our hybrid model. Besides, the proposed prediction models can assist countries in terms of taking important precautions to control the spread of Covid-19 in the world. [ABSTRACT FROM AUTHOR]

Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM) is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM (QHSA-LSSVM) energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of σ and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model (1, 1) (GM (1, 1)), back propagation (BP) and LSSVM, in terms of prediction accuracy and forecasting risk. [ABSTRACT FROM AUTHOR]

Nowadays, we can obtain a huge amount of information and data of railway dangerous goods transportation system through various technical documents, accident reports, monitoring technologies etc. For the space composed of massive historical information and data, a data-driven fault predication approach that can better grasp some important local information while ignoring the negative impact of other data in the global might be practical. However, we have the following challenges: (i) How to reduce the impact of unimportant information on prediction results in advance? (ii) What kind of local approximation data-driven approach should be applied, and (iii) How to correct the results and improve the reliability and accuracy after fault predication based on the local approximation data-driven approach? In this paper, two data-driven approaches including Back Propagation with Markov Correction and Radial Basis Function with Markov Correction are proposed to predicate the fault in a railway dangerous goods transportation system. In order to solve challenge (i), we decompose the whole transportation process into multiple sub-processes based on Work Breakdown Structure with clear duration boundary on the time axis, and use Risk Breakdown Structure to detect the possible faults in each sub-process. In order to solve challenge (ii), we use and compare Back Propagation/Radial Basis Function with global approximation/local approximation and nonlinear prediction ability to learn and predicate the fault based on the collected historical data. In order to solve challenge (iii), we correct the prediction results with fluctuating deviation by using Markov Correction due to its non-aftereffect. Finally, a case study is conducted based on the collected historical fault data of railway dangerous goods transportation system in China. The results show that, for the prediction results based on Back Propagation, the average error increases from 1.43 to 1.88 and the Mean Square Error increases from 3.27 to 5.60 after Markov Correction. For the prediction results based on Radial Basis Function, the average error decreases from 1.13 to 1.11 and the Mean Square Error decreases from 1.81 to 1.76 after Markov Correction. The non-aftereffect of Markov chain well corresponds to the local approximation of Radial Basis Function. When the prediction value is determined by some data of the series, Radial Basis Function with Markov Correction can be applied. When the prediction value is determined by all the data (small-scale) of the series, Back Propagation with Markov Correction is better. • BP-MC and RBF-MC are proposed to predicate the fault in a RDGT system. • WBS-RBS is used to detect possible faults in the whole transportation process. • BP/RBF is applied to learn and predicate the fault based on the historical data. • MC is used to correct the predication results due to its non-aftereffect. • RBF-MC has better fault predication performance than BP-MC. [ABSTRACT FROM AUTHOR]

Water level prediction in large dammed rivers is an important task for flood control, hydropower generation, and ecological protection. The variations of water levels in large rivers are traditionally simulated based on hydrological models. Recently, most studies have begun applying deep learning (DL) models as an alternative method for forecasting the dynamics of water levels. However, it is still challenging to directly apply DL to the simultaneous prediction of water levels across multiple sites. This study attempts to develop a hybrid framework by combining the Physical-based Hydrological model (PHM) and Long Short-Term Memory (LSTM). This study hypothesizes that our hybrid model can enhance the predictive accuracy of water levels in large rivers, because it considers the temporal-spatial information of mainstream-tributaries relationships. The effectiveness of the proposed model (PHM-BP-LSTM) is evaluated using the daily water levels from 2012 to 2018 in the Three Gorges Reservoir (TGR), China. Firstly, we use a hydrological model to produce a large amount of water level data to solve the limited training data set. Then, we use the Back Propagation (BP) neural network to capture the mainstream-tributaries relationship. The future changes in water levels in the different mainstream stations are simultaneously predicted by the LSTM model. We reveal that our hybrid model yields satisfactory accuracy for daily water level simulations at fourteen mainstream stations of the TGR. We further demonstrate the proposed model outperforms the traditional machine learning methods in different prediction scenarios (one-day-ahead, three-day-ahead, seven-day-ahead), with RMSE values ranging from 0.793 m to 1.918 m, MAE values ranging from 0.489 m to 1.321 m, and the average relative errors at each mainstream station are controlled below 4%. Overall, our PHM-BP-LSTM, combining physical process and deep learning, can be viewed as a potentially useful approach for water level prediction in the TGR, and possibly for the rapid forecast of changes in water levels in other large rivers. [ABSTRACT FROM AUTHOR]

The optimal selection of characteristic bands and retrieval models for the hyperspectral retrieval of soil heavy metal concentrations poses a significant challenge. Additionally, satellite-based hyperspectral retrieval encounters several issues, including atmospheric effects, limitations in temporal and radiometric resolution, and data acquisition, among others. Given this, the retrieval performance of the soil arsenic (As) concentration in Pingtan Island, the largest island in Fujian Province and the fifth largest in China, is currently unclear. This study aimed to elucidate this issue by identifying optimal characteristic bands from the full spectrum from both statistical and physical perspectives. We tested three linear models, namely Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Geographically Weighted Regression (GWR), as well as three nonlinear machine learning models, including Back Propagation Neural Network (BP), Support Vector Machine Regression (SVR) and Random Forest Regression (RFR). We then retrieved soil arsenic content using ground-based soil full spectrum data on Pingtan Island. Our results indicate that the RFR model consistently outperformed all others when using both original and optimal characteristic bands. This superior performance suggests a complex, nonlinear relationship between soil arsenic concentration and spectral variables, influenced by diverse landscape factors. The GWR model, which considers spatial non-stationarity and heterogeneity, outperformed traditional models such as BP and SVR. This finding underscores the potential of incorporating spatial characteristics to enhance traditional machine learning models in geospatial studies. When evaluating retrieval model accuracy based on optimal characteristic bands, the RFR model maintained its top performance, and linear models (MLR, PLSR and GWR) showed notable improvement. Specifically, the GWR model achieved the highest r value for the validation data, indicating that selecting optimal characteristic bands based on high Pearson's correlation coefficients (e.g., abs(Pearson's correlation coefficient) ≥0.45) and high sensitivity to soil active materials successfully mitigates uncertainties linked to characteristic band selection solely based on Pearson's correlation coefficients. Consequently, two effective retrieval models were generated: the best-performing RFR model and the improved GWR model. Our study on Pingtan Island provides theoretical and technical support for monitoring and evaluating soil arsenic concentrations using satellite-based spectroscopy in densely populated, relatively independent island towns in China and worldwide. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel divide-and-conquer model for CPI predictionwith the existing compilation method of the Consumer Price Index (CPI) in China. Historical national CPI time series is preliminary divided into eight sub-indexes includingfood, articles for smoking and drinking, clothing, household facilities, articles and maintenance services, health care and personal articles, transportation and communication, recreation, education and culture articles and services, and residence. Three models including back propagation neural network (BPNN) model, grey forecasting model (GM (1, 1)) and autoregressive integrated moving average (ARIMA) model are established to predict each sub-index, respectively.Then the best predicting result among the three models’ for each sub-index is identified. To further improve the performance, special modification in predicting method is done to sub-CPIs whose forecasting results are not satisfying enough. After improvement and error adjustment, we get the advanced predicting results of the sub-CPIs.Eventually, the best predicting results of each sub-index are integrated to form the forecasting results of the national CPI. Empirical analysis demonstrates that the accuracy and stability of the introduced method in this paper is better than many commonly adopted forecasting methods, which indicates the proposed method is an effective and alternative one for national CPI prediction in China. [Copyright &y& Elsevier]

Scarcity or unavailability of precipitation observation creates difficulties in hydrologic modeling of mountainous sections of the arid region of northwest China (34°-50°N, 72°-107°E). Tropical Rainfall Measuring Mission (TRMM) precipitation products may be a potential substitute, but they should be evaluated and corrected with ground observation data before application. In this paper, two TRMM Multisatellite Precipitation Analysis (TMPA) precipitation products were evaluated by gauge observations, using indices such as frequency bias index, probability of detection, false alarm ratio, relative mean bias, Nash-Sutcliffe efficiency, and correlation coefficient. Terrain variables were extracted from a digital elevation model, and their rotated principal components were determined to establish a stepwise regression model to adjust TMPA precipitation. Additionally, a back-propagation (BP) neural network was established to correct TMPA precipitation. The results showed that TMPA had an unsatisfactory detection ability in the study area for both precipitation occurrence and amount. TMPA precipitation corrected by a stepwise regression method showed some improvement, but only the results for TRMM 3B43 on a subregion scale were acceptable. The BP neural network method showed better results than the stepwise regression method, and both TRMM 3B42 and TRMM 3B43 corrected by the former method on a subregion scale could be acceptable. Both methods were spatial-scale dependent and showed better results on a subregion scale than on a larger scale. [ABSTRACT FROM AUTHOR]

It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMESA−B), Y = 0.801x + 16.925 (CMESA−C), and Y = 0.959x + 3.458 (CMESC−D); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R2 are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMESA−B models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMESA−C and CI-BPNN-CMESA−C-CMESC−D models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas. [ABSTRACT FROM AUTHOR]

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model. [ABSTRACT FROM AUTHOR]

Abstract Systems for monitoring the deformation and stress conditions of excavated high rock slopes are usually implemented for safety reasons and to predict the stability of future works. This paper adopts Bayesian methods for updating important geomechanical parameters namely: E (III 1), E (III 2), E (IV 2), E (f 8), c (III 1), c (III 2), c (V 1) and c (f 8) in these types of cases. The proposed method utilizes parametric sensitivity analysis, the BP neural network (back propagation neural network), and Bayesian updating to effectively reduce the number of variables, improve the computational efficiency and gradually update the random variables by using progressive monitoring information. The high rock slope excavation on the left bank at the Lianghekou Hydropower Station in China is illustrated as a detailed case study. Initially, only one type of measurement is first used for Bayesian updating (measured displacements or anchorage forces), and then both types of measurements are used. Compared to using only one type of measurement, the parameter uncertainty is reduced and the model accuracy is improved when both types of measurements are employed. Highlights • A Bayesian updating framework for rock slopes with multiple responses is proposed. • Random variables are gradually updated by using progressive monitoring information. • The effectiveness of three groups of data in Bayesian updating is discussed. • The proposed framework can be used for the prediction of subsequent excavation step. [ABSTRACT FROM AUTHOR]

Apple is an important cash crop in China, and the prediction of its freshness can effectively reduce its storage risk and avoid economic loss. The change in the concentration of odor information such as ethylene, carbon dioxide, and ethanol emitted during apple storage is an important feature to characterize the freshness of apples. In order to accurately predict the freshness level of apples, an electronic nose system based on a gas sensor array and wireless transmission module is designed, and a neural network prediction model using an improved Sparrow Search Algorithm (SSA) based on chaotic sequence (Tent) to optimize Back Propagation (BP) is proposed. The odor information emitted by apples is studied to complete an apple freshness prediction. Furthermore, by fitting the relationship between the prediction coefficient and the input vector, the accuracy benchmark of the prediction model is set, which further improves the prediction accuracy of apple odor information. Compared with the traditional prediction method, the system has the characteristics of simple operation, low cost, reliable results, mobile portability, and it avoids the damage to apples in the process of freshness prediction to realize non-destructive testing. [ABSTRACT FROM AUTHOR]

Arch bridges play an important role in rural roads in China. Due to insufficient funds and a lack of management techniques, many rural arch bridges are in a state of disrepair, unable to meet the increasing transportation needs. Thus, it is of great significance to develop a set of rapid and economic damage identification procedures for the management and maintenance of old arch bridges. Sanliushui Bridge, located in Chenggu County, Hanzhong, is selected as a model case. Field tests and numerical simulations were carried out to identify the damage states of Sanliushui Bridge. Wavelet Packet Energy change Rate Sum Square (WPERSS), a damage identification index based on wavelet packet analysis method was implemented to process the measured data of the load test and the simulated data of the numerical calculation model with assumed damage. Back Propagation Neural Network (BPNN), Genetic Algorithm-based BPNN (GA-BPNN), Particle Swarm Optimization Algorithm-based BPNN (PSO-BPNN) approaches and test data analysis are adopted to compare the measured data with the simulated data to quantitatively identify the damage degree of the selected bridge. By comparing the results of the two methods mentioned above, it is found that the proposed damage identification approach realized a precise damage identification of the selected arch bridges. [ABSTRACT FROM AUTHOR]

The issue of urban air quality in China has become increasingly significant due to industrialization and rapid urbanization. Although PM2.5 is the major air pollutant in most cities of northern China and has a direct negative impact on human health, there is a problem of under-representativeness at Chinese monitoring stations. In some cities, due to the relatively fewer national control stations and the fact that the stations are located closer to pollution sources, under the current assessment system, the monitoring data are not sufficient for the fairness of air quality assessment in different cities. In this article, the multispectral data of Landsat 8 data, air quality data, and meteorological data from ground monitoring stations have been integrated together and imported to different PM2.5-estimation models established based on the multi-layer back propagation neural network (MLBPN), support vector regression (SVR), and random forest (RF), respectively. According to the evaluation indices of R2, RMSE, and ME, the estimation model based on the MLBPN revealed the best PM2.5 estimation results and was therefore employed for the regional representativeness analysis in the study area of Xi'an, Shaanxi, China. The annual average PM2.5 concentration in the study area is depicted after error correction using Kriging interpolation, which can be further used to evaluate and analyze the representativeness of monitoring stations in Xi'an. By calculating the difference between the actual station annual average and the annual average of estimated PM2.5 concentration in the whole region, it can be found that the regional annual average value of PM2.5 in Xi'an is overestimated. To sum up, this article proposes a feasible method for the spatial positioning of the air quality monitoring stations to be established. [ABSTRACT FROM AUTHOR]

Lake turbidity, representing a general indicator of water 'cloudiness', is a key parameter in many monitoring programs. It is not possible to cover all lakes with frequent in situ monitoring. Sentinel-2 MultiSpectral Imager (MSI) can help to fill the gaps if a robust turbidity retrieval methodology is developed. Previously published results demonstrated the usefulness of MSI at a limited regional scale, while our aim was to develop methodology that allows monitoring turbidity over the whole of China. We proposed methodology with a reflectance that can be classified into optical water types (OWTs), and then a back propagation neural network model (BP-TURB) is used to estimate turbidity. The reflectance of in situ lake samples extracted from MSI imagery was clustered as three OWTs, and validation performance was satisfactory: R2 > 0.81, RMSE < 17.54, and MAE < 11.20. This allowed us to map turbidity in all Chinese lakes, of which the area is larger than 1 km2. A larger percentage of clear lakes (53.26%) with low turbidity levels (<10 NTU) was found in 2020 than in 2015 (37.43%). Lakes in the plateau regions generally exhibited lower turbidity than those situated in the plains regions, for which the turbidity patterns were determined by lake volume, averaged depth, and elevation. We demonstrated that the Sentinel-2 MSI data with the novel approach proposed by us allows for mapping lake turbidity over a large variety of lakes and extensive geographic conditions, as well as for revealing temporal changes in these lakes and their links to lake abiotic characteristics. [ABSTRACT FROM AUTHOR]

Food safety problems are becoming increasingly severe in modern society, and establishing an accurate food safety risk warning and analysis model is of positive significance in avoiding food safety accidents. We propose an algorithmic framework that integrates the analytic hierarchy process based on the entropy weight (AHP-EW) and the autoencoder-recurrent neural network (AE-RNN). Specifically, the AHP-EW method is first used to obtain the weight percentages of each detection index. The comprehensive risk value of the product samples is obtained by weighted summation with the detection data, which is used as the expected output of the AE-RNN network. The AE-RNN network is constructed to predict the comprehensive risk value of unknown products. The detailed risk analysis and control measures are taken based on the risk value. We applied this method to the detection data of a dairy product brand in China for example validation. Compared with the performance of 3 models of the back propagation algorithm (BP), the long short-term memory network (LSTM), and the LSTM based on the attention mechanism (LSTM-Attention), the AE-RNN model has a shorter convergence time, predicts data more accurately. The root mean square error (RMSE) of experimental data is only 0.0018, proving that the model is feasible in practice and helps improve the food safety supervision system in China to avoid food safety incidents. [ABSTRACT FROM AUTHOR]

The over-exploitation of groundwater has led to a significant drop in groundwater levels, which may lead to a series of geological disasters and ecological environmental problems such as ground subsidence and ground cracks. Therefore, through studying the dynamic change characteristics of groundwater, we can grasp the dynamic changes in groundwater level over time and invert the hydrogeological parameters, which provides an important basis for the management of groundwater resources. In this study, the confined aquifer III groundwater between 2005 and 2014 in Yancheng City was selected as the research object, and the Back Propagation (BP) neural network, Spatial-temporal Auto Regressive and Moving Average (STARMA) model, and BP-STARMA model were used to predict the spatial and temporal evolution trends of groundwater. In order to compare the prediction effectiveness of the BP-STARMA model, the fitting and prediction accuracies of the three models were measured from the perspectives of time and space. The results of the Relative Squared Error (RSE), Normal Mean Squared Error (NMSE), Root-Mean-Squared Error (RMSE), and Mean Absolute Error (MAE) were used to assess the robustness of the BP-STARMA model. The results showed that the fitting of the RMSE of BP-STARMA model was reduced by 39.92%, 38.35%, 30.25%, 31.55%, and 13.57% compared with the STARMA model, and by 22.2%, 8.7%, 15.9%, 28.5%, and 4.42% compared with the BP neural network model, respectively. Collectively, this shows that the BP-STARMA model has a better spatiotemporal prediction of groundwater level than the STARMA and BP neural network models, is more applicable to spatially continuous time-discrete spatiotemporal sequences, and is more applicable to spatiotemporal sequences that respond to natural geographic phenomena. [ABSTRACT FROM AUTHOR]

Forecasting the industrial water demand accurately is crucial for sustainable water resource management. This study investigates industrial water demand forecasting by case-based reasoning (CBR) in an arid area, with a case study of Zhangye, China. We constructed a case base with 420 original cases of 28 cities in China, extracted six attributes of the industrial water demand, and employed a back propagation neural network (BPN) to weight each attribute, as well as the grey incidence analysis (GIA) to calculate the similarities between target case and original cases. The forecasting values were calculated by weighted similarities. The results show that the industrial water demand of Zhangye in 2030, which is the t arget case, will reach 11.9 million tons. There are 10 original cases which have relatively high similarities to the target case. Furthermore, the case of Yinchuan, 2010, has the largest similarity, followed by Yinchuan, 2009, and Urumqi, 2009. We also made a comparison experiment in which case-based reasoning is more accurate than the grey forecast model and BPN in water demand forecasting. It is expected that the results of this study will provide references to water resources management and planning. [ABSTRACT FROM AUTHOR]

As a basic natural and strategic resource, water is of great significance to the sustainable development of economies and societies. Owing to population growth, industrialization, the acceleration of urbanization, and global warming, water poverty is gradually increasing in some parts of the world. Effectively assessing water poverty from different dimensions is still a serious challenge for global water resources planning. This paper establishes a framework of multidimensional water poverty (MWP) from six dimensions: water management, water technology, water assets, water welfare, water resources, and water environment. The measurement model of MWP is built based on the Back Propagation Neural Network (BPNN), and the Spatial Correlation Analysis tool is used to visualize the spatial effects of MWP. The Yangtze River Economic Belt (YREB) was used as a case study and the main factors affecting the MWP of the YREB were determined by the Geodetector. When analyzing the results the following observations were made: (1) In terms of time distribution, the level of MWP in the YREB has gradually increased, and the poverty gap between the upper reaches, as well as the middle and lower reaches, shows an increasing trend. (2) With respect to spatial distribution, there is a continuously increasing agglomeration effect that shows a gradient-increasing distribution pattern of "West-Central-East." (3) The MWP in the YREB is mainly affected by these indicators in the three dimensions consisting of water resources, water technology, and water management. Specifically, R&D expenditure as a percentage of GDP, the proportion of water-saving irrigation area in the cultivable land area, the urban daily wastewater treatment capacity, the land surface water resources per capita, and the groundwater resources per capita play an important role in the MWP. Based on the above findings, targeted policy recommendations are proposed to alleviate the MWP in the YREB. • Model of multidimensional water poverty developed to assess water scarcity. • Multidimensional water poverty and its agglomeration effect in the YREB increases. • Differences in multidimensional water poverty for the YREB's three reaches are clear. [ABSTRACT FROM AUTHOR]

This study proposes a decomposed broad learning model to improve the forecasting accuracy for tourism arrivals on Hainan Island in China. With decomposed broad learning, we predicted monthly tourist arrivals from 12 countries to Hainan Island. We compared the actual tourist arrivals to Hainan from the US with the predicted tourist arrivals using three models (FEWT-BL: fuzzy entropy empirical wavelet transform-based broad learning; BL: broad Learning; BPNN: back propagation neural network). The results indicated that US foreigners had the most arrivals in 12 countries, and FEWT-BL had the best performance in forecasting tourism arrivals. In conclusion, we establish a unique model for accurate tourism forecasting that can facilitate decision-making in tourism management, especially at turning points in time. [ABSTRACT FROM AUTHOR]

Urban agglomerations, such as Beijing-Tianjin-Hebei Region, Yangtze River Delta and Pearl River Delta, are the key regions for energy conservation, carbon emission reduction and low-carbon development in China. However, spatiotemporal patterns of CO2 emissions at fine scale in these major urban agglomerations are not well documented. In this study, a back propagation neural network based on genetic algorithm optimization (GABP) coupled with NPP/VIIRS nighttime light datasets was established to estimate the CO2 emissions of China's three major urban agglomerations at 500 m resolution from 2014 to 2019. The results showed that spatial patterns of CO2 emissions presented three-core distribution in the Beijing-Tianjin-Hebei Region, multiple-core distribution in the Yangtze River Delta, and null-core distribution in the Pearl River Delta. Temporal patterns of CO2 emissions showed upward trends in 28.74–43.99% of the total areas while downward trends were shown in 13.47–15.43% of the total areas in three urban agglomerations. The total amount of CO2 emissions in urban areas was largest among urban circles, followed by first-level urban circles and second-level urban circles. The profiles of CO2 emissions along urbanization gradients featured high peaks and wide ranges in large cities, and low peaks and narrow ranges in small cities. Population density primarily impacted the spatial pattern of CO2 emissions among urban agglomerations, followed by terrain slope. These findings suggested that differences in urban agglomerations should be taken into consideration in formulating emission reduction policies. [ABSTRACT FROM AUTHOR]