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Aims: The purpose of this study was to identify risk factors for cognitive impairment in advanced cancer patients and to develop predictive models based on these risk factors. Background: Cancer‐related cognitive impairment seriously affects the quality of life of advanced cancer patients. However, neural network models of cognitive impairment in patients with advanced cancer have not yet been identified. Design: A cross‐sectional design was used. Methods: This study collected 494 questionnaires between January and June 2022. Statistically significant clinical indicators were selected by univariate analysis, and the artificial neural network model and logistic regression model were used for multivariate analysis. The predicted value of the model was estimated using the area under the subject's working characteristic curve. Result: The artificial neural network and the logistic regression models suggested that cancer course, anxiety and age were the major risk factors for cognitive impairment in advanced cancer patients. All the indexes of artificial neural network model constructed in this study are better than those of the logistic model. Conclusion: The artificial neural network model can better predict the risk factors of cognitive impairment in patients with advanced cancer. Better prediction will enable nurses and other healthcare professionals to provide better targeted and timely support. Summary statement: What is already known about this topic?: Cancer‐related cognitive impairment has significantly affected the quality of life of patients with advanced cancer.Risk factors associated with cognitive impairment in patients with advanced cancer have not been adequately documented, and neural network models have not been identified. What this paper adds?: This study found that long a cancer course, anxiety and ageing were the most important risk factors for cognitive impairment in patients with advanced cancer.The artificial neural network showed good predictive indicators, indicating that it can be used to predict cognitive impairment in patients with advanced cancer. The implications of this paper: Neural network models can help nurses and other clinicians predict cognitive impairment in advanced cancer patients early which will allow provision of timely intervention. [ABSTRACT FROM AUTHOR]

Although the Chinese government has shifted from a one-child policy to a two-child policy (allowing a couple to have up to two children) since 2016 in response to the aging population, the policy results have been unsatisfactory. This is the first paper to systematically investigate the factors influencing residents' intentions to have a second child. The research focuses on the perspective of individual, family, and social characteristics based on the Chinese General Social Survey (CGSS) from 2017 to 2018. Three machine learning methods are used in conjunction with logistic regression to reveal that the intention of having a second child increases heavily with age, more siblings in the family of origin, and better health. The family income, which is currently the focus of the literature and is statistically significant, is only sixth most important. This study further reveals differences between genders: Women with a lower level of education and religious beliefs prefer to have a second child, whereas for men, non-agricultural hukou and marriage are the position factors. The results of this study also illustrate the importance of future research focusing on the relationship of individuals to their family of origin and districts. [ABSTRACT FROM AUTHOR]

In this paper, a novel ANN flood forecasting model is proposed. The ANN model is combined with traditional hydrological concepts and methods, taking the initial Antecedent Precipitation Index (API), rainfall, upstream inflow and initial flow at the forecast river section as input of model, and flood flow forecast of the next time steps as output of the model. The distributed rainfall is realized as the input of the model. The simulation is processed by dividing the watershed into several rainfall-runoff processing units. Two hidden layers are used in the ANN, and the topology of ANN is optimized by connecting the hidden layer neurons only with the input which has physical conceptual causes. The topological structure of the proposed ANN model and its information transmission process are more consistent with the physical conception of rainfall-runoff, and the weight parameters of the model are reduced. The arithmetic moving-average algorithm is added to the output of the model to simulate the pondage action of the watershed. Satisfactory results have been achieved in the Mozitan and Xianghongdian reservoirs in the upper reaches of Pi river in Huaihe Basin, and the Fengman reservoir in the upper reach of Second Songhua river in Songhua basin in China. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) management is a crucial issue in socioeconomic metabolism and requires multicategory and high‐resolution data, and data on the physical composition of municipal solid waste (PCMSW) are fundamental in MSW research. Extensive financial resources have been invested in the research on field investigations of PCMSW; however, it is time‐consuming and sometimes not truly representative of the studied case. In this work, two bottom‐up and two top‐down approaches were applied for analyzing the PCMSW, namely, field investigation (FI), BP neural network (BPNN), material flow analysis (MFA), and inversion algorithm based on electricity generation of waste incinerator (IAEI). Wuhan City, China, was chosen as the studied case for analyzing and comparing the PCMSW results. The PCMSW values obtained by applying those four methods showed acceptable differences, and the standard deviations of organic fraction, ash and stone, paper, plastic and rubber, textile, wood, metal, glass, and others were 3.94%, 2.77%, 6.57%, 2.22%, 2.49%, 1.36%, 0.53%, 1.19%, and 0.28%, respectively. Use of the MFA, BPNN, and IAEI methods could reduce time and labor spent on manual sampling, sorting, and weighing of MSW, compared to the FI method. BPNN algorithm advances in providing PCMSW data in history and by trajectory throughout the year, whereas IAEI contributes PCMSW data with much higher temporal resolution. Data quality and applicability of different methods were discussed, considering the availability of time, labor, and reference data. [ABSTRACT FROM AUTHOR]

Traditionally, temperature control for massive concrete structures during the construction phase is based on experience instead of using advanced methods such as predictive control method. In this paper, a predictive control method with a hybrid data-based artificial neural network (ANN) model is proposed. The main parameters of the numerical model are identified based on the data measured from the site. Since the limitation of the measured data, a rich dataset is generated by the numerical model with the identified parameters. Finally, a hybrid data-based ANN model for predicting the temperature indicator of massive concrete is developed. A case study in which the temperature control of concrete in a real bridge anchorage foundation construction in Southwestern China is conducted. Results show that the maximum temperature of concrete can be controlled around the target temperature by the proposed method. The average temperature decline rate decreases from 0.19 °C/h to 0.14 °C/h in the period of 120 h (i.e., from 60 h to 180 h after the concrete pouring) at a sudden decrease of 8 °C in environmental temperature, thereby enabling a significant reduction in the risk of concrete cracking. [ABSTRACT FROM AUTHOR]

In this paper, a Comprehensive Diagram Method (CDM) for a Multi-Layer Perceptron Neuron Network (MLPNN) is proposed to realize natural gas energy metering using temperature, pressure, and the speed of sound from an ultrasonic flowmeter. Training and testing of the MLPNN model were performed on the basis of 1003 real data points describing the compression factors (Z-factors) and calorific values of the three main components of natural gas in Sichuan province, China. Moreover, 20 days of real tests were conducted to verify the measurements' accuracy and the adaptability of the new intelligent method. Based on the values of the Mean Relative Errors and the Root Mean Square errors for the learning and test errors calculated on the basis of the actual data, the best-quality MLP 3-5-1 network for the metering of Z-factors and the new CDM methods for the metering of calorific values were experimentally selected. The Bayesian regularized MLPNN (BR-MLPNN) 3-5-1 network showed that the Z-factors of natural gas have a maximum relative error of −0.44%, and the new CDM method revealed calorific values with a maximum relative error of 1.90%. In addition, three local tests revealed that the maximum relative error of the daily cumulative amount of natural gas energy was 2.39%. [ABSTRACT FROM AUTHOR]

Forecasting the output power of solar PV systems is required for the good operation of the power grid and the optimal management of energy fluxes occurring in the solar system. Before forecasting the solar system's output, it is essential to focus on the prediction of solar irradiance. In this paper, the solar radiation data collected for two years in a certain place in Jiangsu in China are investigated. The objective of this paper is to improve the ability of short-term solar radiation prediction. Firstly, missing data are recovered through the means of matrix completion. Then the completed data are denoised via robust principal component analysis. To reduce the influence of weather types on solar radiation, spectral clustering is adopted by fusing sparse subspace representation and k-nearest-neighbor to partition the data into three clusters. Next, for each cluster, four neural networks are established to predict the short-term solar radiation. The experimental results show that the proposed method can enhance the solar radiation accuracy. [ABSTRACT FROM AUTHOR]

In the new normal period, the trend changes and adjustments of the environment such as international trade, production capacity, labor supply and resource constraints have put forward new requirements for China's industrial development, which have brought new challenges and given new opportunities. In the new normal stage where economic growth continues to decline, industrial growth is still an important support for economic growth. The advancement of industrial technology is the main driving force for improving the total factor productivity of the industrial industry. Therefore, the most important thing to promote industrial growth is to upgrade the level of industrial technology. In response to the above-mentioned problems, this paper analyzed the relationship between industrial technology and industrial output in the new normal environment by using the BP neural network (BPNN) algorithm. The connection between the two has been found, which provided a clear direction for the functional adjustment of economic law. Experimental studies have shown that there is a positive relationship between industrial technological progress and industrial output. When other conditions are the same, and when the non-new normal is selected, industrial output increases by about 0.36% for every 1% increase in industrial technological progress. When choosing to be in the new normal, industrial technological progress has a higher impact on industrial output. For every 1% increase in technological progress, industrial output increases by about 0.39%. [ABSTRACT FROM AUTHOR]

A fiber-optic gyroscope (FOG) with lower precision but higher cost advantage is typically selected according to working conditions and engineering budget. Thermal drift is the main factor affecting FOG precision. External thermal calibration methods by algorithms can effectively weaken the influence of thermal drift. This paper presents a thermal calibration method of a two-dimensional N-order polynomial (TDNP) and compares it with artificial neural network (ANN) methods to determine a software FOG thermal calibration method for landslide displacement monitoring. The TDNP thermal calibration coefficient matrix was established, and the thermal calibration capability of the TDNP method with different orders N was evaluated on the basis of error analysis. The ANN model with 1 to 18 hidden neural layers was established on the basis of LM, BR, and SCG algorithms to choose a suitable ANN. Finally, the mean absolute errors of FOG thermal calibration through the TDNP with different orders and the LM were compared. This method was applied in the Huangtupo landslide area, China. The results highlight that the TDNP method with order 5 had better performance and satisfied the requirements of landslide displacement monitoring. The research results can compensate for the lack of adaptability of the FOG thermal calibration method in landslide displacement monitoring. [ABSTRACT FROM AUTHOR]

We thank Zhang et al. (Nat Resour Res, 2019. 10.1007/s11053-019-09512-6) for investigating the accuracy of artificial intelligence techniques in the prediction of drought in China. In the paper by Zhang et al. (2019), two data-driven models, namely artificial neural network and support vector machine, and autoregressive integrated moving average (ARIMA) model were established to estimate standardized precipitation evapotranspiration index (SPEI) values. In that paper, temperature and precipitation values were used as independent variables to predict SPEI. They stated that ARIMA models give higher accuracy in the prediction of SPEI values. Here, not only some of the missing points and deficiencies in the original publication, but also improvements that can be made in future studies, were mentioned. In addition, several points are introduced in order to make these points more clarified for potential readers. [ABSTRACT FROM AUTHOR]

As a supplement to the financing of small- and medium-sized enterprises, bank microfinance companies are nonbank financial institutions, and it has played an active role in maintaining the stability of financial markets. However, in the course of the operation of microfinance companies, due to the lack of careful management and risk control, the problem of risk management has become increasingly prominent. The purpose of this paper is to study the microfinance risk based on polygenic artificial neural network, and it provides theory and practice application for risk management of microcredit enterprises. Taking the risk management of China's microfinance companies as the research object, on the basis of previous studies, this paper analyzes the risks of bank microfinance companies. Secondly, the basic theory of neural network model and its transformation function are introduced, and the learning method of neural network. At the same time, the learning algorithm of neural network and its improved algorithm are mainly introduced. It lays a theoretical foundation for the follow-up empirical research. Then, through the empirical study of data-based risk assessment of microcredit of farmers, the sample data are divided into training samples and test samples for comparison. Then, we use MATLAB software to establish a neural network model for farmers' microcredit risk assessment. Finally, in order to make the neural network model of farmers' credit risk assessment better popularize and apply, and effectively reduce the credit risk of farmers' microcredit. The corresponding policy suggestions are put forward, which proves the validity and applicability of the neural network in the field of farmers' microcredit risk assessment. It provides a good basis for rural credit cooperatives to identify the credit risk of farmers. [ABSTRACT FROM AUTHOR]

With the steady growth of the economy and the rapid development of modern industrial technology, the problem of environmental pollution has increased. To continue to develop, it is necessary to thoroughly implement the sustainable development strategy, and we must pay more attention to environmental issues. One of the important management tools implemented in China for environmental management is environmental quality monitoring and evaluation. Environmental quality monitoring can scientifically evaluate the environmental quality of a region, scientifically evaluate and forecast the environmental management and environmental engineering, and provide scientific basis for environmental management, environmental engineering, formulation of environmental standards, environmental planning, comprehensive prevention and control of environmental pollution, and ecological environment construction. This paper will discuss the basic principles of neural network and the implementation process of MATLAB and in the MATLAB software implementation and display process. At the same time, the results of different parameters are analyzed through experiments, and the network parameters are constantly adjusted to improve the accuracy of the evaluation results. Taking the regional environment as an example, two monitoring methods are proposed, and a variety of neural network models are used to analyze each prediction method. Case study results show that the latter method has a better prediction effect. [ABSTRACT FROM AUTHOR]

Current housing unit design focuses on performance optimization, such as building energy use, daylighting, and occupant comfort. There is significant potential for efficient optimization in the early design stage. This paper proposed a building energy demand optimization framework that considers building design variables for housing unit design in Beijing. First, a high-rise apartment building was designed using geometric and thermal envelope parameters. Second, a thermal load simulation of the housing unit was performed. Third, sensitivity analysis and multi-objective optimization were conducted to evaluate the building's performance. Finally, a neural network was trained to predict the thermal load. A comparison of the base case and optimal cases revealed that more than 20% of energy demand could be saved. In addition, the effect of the parameters on the thermal load and the lighting schedules on the unit's energy load were analyzed. [ABSTRACT FROM AUTHOR]

• An upscaling regional photovoltaic power forecasting scheme based on ANN is proposed. • A novel method of reference PV plants selection is proposed. • A power correction method is proposed to guarantee power forecasting accuracy. • The rolling forecasting accuracy is improved by integrating multiple ANN models. • The forecasting error is 4.04%, 3.45% and 2.86% in resolution of 1, 5 and 10mins. Along with the increasing penetration of photovoltaic (PV) power generation, regional power forecasting becomes more and more critical for stable and economical operation of power system. The key challenge of regional PV power forecasting technology is the lack of complete and accurate historical power data since not all PV plants are equipped with the precise real-time output power monitoring system. Besides, the computation burden will be heavy when the number of PV plants in the target region is large. This paper therefore proposes an upscaling minute-level regional PV power forecasting scheme using the data of the selected reference PV plants. In this paper, a novel method of reference PV plants selection is proposed by comprehensively considering the prediction accuracy of artificial neural network (ANN) as well as Pearson correlation coefficient. The reference PV plant selection coefficient μ is introduced as the comprehensive indicator for reference PV plant selection, which incorporates Pearson correlation coefficient and MAPE. In addition, a PV output power correction method is assumed to guarantee the proper operation of regional power forecasting. Besides, this paper proposes a flexible approach to effectively decrease the accumulated error of rolling forecasting by integrating the forecasting results under different temporal resolutions. In specific, the power forecasting results in temporal resolutions of 1 min, 5 min and 15 min are simultaneously derived and the performance between the traditional rolling forecasting and the proposed method is compared. The validity of the proposed method is finally verified using the collected historical power data of PV plants installed in a city of Eastern China. For time resolution of 1 min, 5 mins and 10 mins, the corresponding RMSE are 6.56, 5.73 and 4.85 and corresponding MAPE are 4.04%, 3.45% and 2.86%. [ABSTRACT FROM AUTHOR]

Partner selection is a fundamental issue in supply chain management as it contributes significantly to overall supply chain performance. However, such decision-making is problematic due to the need to consider both tangible and intangible factors, which cause vagueness, ambiguity and complexity. This paper proposes a new fuzzy intelligent approach for partner selection in agile supply chains by using fuzzy set theory in combination with radial basis function artificial neural network. Using these two approaches in combination enables the model to classify potential partners in the qualification phase of partner selection efficiently and effectively using very large amounts of both qualitative and quantitative data. The paper includes a worked empirical application of the model with data from 84 representative companies within the Chinese electrical components and equipment industry, to demonstrate its suitability for helping organisational decision-makers in partner selection. [ABSTRACT FROM AUTHOR]

Urbanization, especially in developing countries, has led to numerous concerns, such as air pollution, traffic congestion and habitat destruction. Within this context, it is important to evaluate urban development as sustainable, and various sustainability assessment methods have been developed, including fuzzy logic approaches. However, predefined fuzzy rules and simple linear membership functions were used, which are largely based on the knowledge of subject experts. Therefore, this paper aims to introduce an adaptive neuro‐fuzzy inference systems (ANFIS) approach for urban sustainability assessment. With collected training samples from the Urban China Initiative, and the ANFIS approach was used to rank 185 selected cities in China. The results show that the ANFIS approach is appropriate for assessing urban sustainability, and the nonlinear membership functions fit the training samples better than the linear membership functions. Further discussion indicates that future research on sustainability assessment should be more integrated. [ABSTRACT FROM AUTHOR]

The objective of this paper is to optimize the passive design of newly-built residential buildings in hot summer and cold winter region of China for improving indoor thermal comfort while reducing building energy demand. In this respect, this paper investigates the performance of a representative apartment building in the city of Shanghai and evaluates the optimum solutions by using a developed optimization approach, which includes three major steps of 1) setting the model for multi-objective optimization, 2) sensitivity analysis for reducing the dimension of input variables, and 3) multi-objective optimization by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the Artificial Neural Network (ANN), among which a novel indicator for evaluating the annual indoor thermal comfort of residential buildings of Shanghai named Comfort Time Ratio (CTR) is defined based on the modification of Szokolay’s theory in terms of bioclimatic analysis, and the impacts of passive design variables on the indoor thermal comfort and building energy demand in terms of different directions are comprehensively investigated. Results of the multi-objective optimization indicate that the residential buildings of Shanghai have a great potential in comfort-improvement and energy-saving. A series of novel optimal passive design tactics for residential buildings in Shanghai are derived accordingly which could be easily understood and conveniently carried out by the architects in practice. [ABSTRACT FROM AUTHOR]

With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014-December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy. [ABSTRACT FROM AUTHOR]

Residential energy retrofitting projects in the hot summer and cold winter (HSCW) zone of China face various risks related to project activities, which incur transaction costs (TCs), such as search, negotiation, and monitoring costs. As the leader in project implementation, the Chinese government is responsible for project planning, organisation, and coordination. However, TCs impede the government's ability to execute risk-related project activities effectively, subsequently increasing the probability of the occurrence of risk. Drawing on transaction cost economics (TCE), this study proposes a theoretical framework to understand the barriers—such as asset specificity, uncertainty, and frequency—that prevent the government from performing project activities and mitigating risks effectively. An artificial neural network (ANN) is applied to verify the hypotheses. The results underscore experience and operational maturity in project activities, cost and time constraints, and the immature retrofitting market as significant impediments to the government's execution of risk-related activities. Considering the varying roles of the government in reducing different risks, this study concludes by offering policy recommendations to alleviate these activity barriers and mitigate risks. By employing a TCs perspective, this study not only identifies key barriers but also deepens our understanding of risk mitigation mechanisms, providing robust policy insights tailored to the specific regional context of China, thereby enhancing both the execution and the framework of government-led retrofitting projects. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LUCC) prediction of cities in Western China requires higher accuracy in quantitative demand and spatial layout because of complex challenges in balancing relationships between urban constructions and ecological developments. Considering city-level areas and various types of land use and land cover, existing LUCC models without constraint or with only loose demand constraints were impractical in providing evidence of high accuracy and high-resolution predictions in areas facing fierce land competition. In this study, we proposed a two-layer SD-ANN-CA model to simulate and explore the LUCC trend and layout predictions for 2018, 2028, and 2038 in Ya'an City, Western China. The two-layer structure with an upper layer of the SD model and a lower layer of the ANN-CA model, as well as the advantages of all three methods of system dynamics (SD), artificial neural network (ANN), and cellular automata (CA), have allowed us to consider the macro-level demand constraints, meso-level driving factors constraints, and the micro-level spatial constraints into a unified model framework. The simulation results of the year 2018 have shown significant improvement in the accuracy of the ANN-CA model constructed in our earlier work, especially in types of forest land (error-accuracy: 0.08%), grassland (error-accuracy: 0.23%), and construction land (error-accuracy: 0.18%). The layout predictions of all six types of land use in 2028 and 2038 are then carried out to provide visual evidence support, which may improve the efficiency of planning and policy-making processes. Our work may also provide insights into new ways to combine quantitative methods into spatial methods in constructing city-level or even regional-level LUCC models with high resolution. [ABSTRACT FROM AUTHOR]

Precise regional ionospheric total electron content (TEC) models play a crucial role in correcting ionospheric delays for single-frequency receivers and studying variations in the Earth's space environment. A particle swarm optimization neural network (PSO-NN)-based model for ionospheric TEC over China has been developed using a long-term (2008–2021) ground-based global positioning system (GPS), COSMIC, and Fengyun data under geomagnetic quiet conditions. In this study, a spatial gridding approach is utilized to propose an improved version of the PSO-NN model, named the PSO-NN-GRID. The root-mean-square error (RMSE) and mean absolute error (MAE) of the TECs estimated from the PSO-NN-GRID model on the test data set are 3.614 and 2.257 TECU, respectively, which are 7.5% and 5.5% smaller than those of the PSO-NN model. The improvements of the PSO-NN-GRID model over the PSO-NN model during the equinox, summer, and winter of 2015 are 0.4–22.1%, 0.1–12.8%, and 0.2–26.2%, respectively. Similarly, in 2019, the corresponding improvements are 0.5–13.6%, 0–10.1%, and 0–16.1%, respectively. The performance of the PSO-NN-GRID model is also verified under different solar activity conditions. The results reveal that the RMSEs for the TECs estimated by the PSO-NN-GRID model, with F10.7 values ranging within [0, 80), [80, 100), [100, 130), [130, 160), [160, 190), [190, 220), and [220, +), are, respectively, 1.0%, 2.8%, 4.7%, 5.5%, 10.1%, 9.1%, and 28.4% smaller than those calculated by the PSO-NN model. [ABSTRACT FROM AUTHOR]

Background: Camellia oleifera, an essential woody oil tree in China, propagates through grafting. However, in production, it has been found that the interaction between rootstocks and scions may affect fruit characteristics. Therefore, it is necessary to predict fruit characteristics after grafting to identify suitable rootstock types. Methods: This study used Deep Neural Network (DNN) methods to analyze the impact of 106 6-year-old grafting combinations on the characteristics of C.oleifera, including fruit and seed characteristics, and fatty acids. The prediction of characteristics changes after grafting was explored to provide technical support for the cultivation and screening of specialized rootstocks. After determining the unsaturated fat acids, palmitoleic acid C16:1, cis-11 eicosenoic acid C20:1, oleic acid C18:1, linoleic acid C18:2, linolenic acid C18:3, kernel oil content, fruit height, fruit diameter, fresh fruit weight, pericarp thickness, fresh seed weight, and the number of fresh seeds, the DNN method was used to calculate and analyze the model. The model was screened using the comprehensive evaluation index of Mean Absolute Error (MAPE), determinate correlation R2 and and time consumption. Results: When using 36 neurons in 3 hidden layers, the deep neural network model had a MAPE of less than or equal to 16.39% on the verification set and less than or equal to 13.40% on the test set. Compared with traditional machine learning methods such as support vector machines and random forests, the DNN method demonstrated more accurate predictions for fruit phenotypic characteristics, with MAPE improvement rates of 7.27 and 3.28 for the 12 characteristics on the test set and maximum R2 improvement values of 0.19 and 0.33. In conclusion, the DNN method developed in this study can effectively predict the oil content and fruit phenotypic characteristics of C. oleifera, providing a valuable tool for predicting the impact of grafting combinations on the fruit of C. oleifera. [ABSTRACT FROM AUTHOR]

In this work, firstly we propose an artificial neural network (ANN) based channel modeling and simulation framework to playback a measurement channel to overcome the shortcomings of traditional geometry based stochastic modelling (GBSM) and simulation approach which is unable to predict a time or position-varying channel to match with real environment. Secondly, we implement the framework based on channel measurements performed at 28 GHz in a large waiting hall at Qingdao high-speed railway station, China. Thirdly, we validate the proposed framework by comparisons of the large scale channel parameters (LSCPs) and small scale channel parameters (SSCPs) extracted from the measured, ANN and GBSM simulation channels. The results show that the ANN-based framework can playback the measured channels accurately, while GBSM-based simulated channels have large deviations. This work offers a solution to playback the measured channels accurately to be used in 5G and beyond radio system research and engineering applications, while it’s also able to be applied in future channel predictions in case of large amount of measured data available. [ABSTRACT FROM AUTHOR]

Wind speed prediction is an important part of the wind farm management and wind power grid connection. Having accurate prediction of short -term wind speed is the basis for predicting wind power. This paper proposes a short -term wind speed prediction strategy based on the wavelet analysis and the multilayer perceptual neural network for the Dabancheng area, in China. Four wavelet neural network models using the Morlet function as the wavelet basis function were developed to forecast short -term wind speed in January, April, July, and October. Predicted wind speed was compared across the four models using the mean square error and regression. Prediction accuracy of model 4 was high, satisfying the forecasting wind power industry requirements. Therefore, the proposed algorithm could be applied for practical short -term wind speed predictions. [ABSTRACT FROM AUTHOR]

Assessing river health poses a significant challenge due to its dependency on multiple hydrological, chemical, and biological factors. While several methods have been presented, a comprehensive and systematic assessment of a river's ecological health necessitates the integration of various factors. In this paper, we propose a novel methodology to establish river health assessing system that leverages expert knowledge and objective indices. With the employment of text mining techniques, we identify five key groups of evaluation indices related to river health. Subsequently, 17 indices for river evaluation are determined by combining current regional metrics and relevant findings from recent related studies. Considering the interactive evaluation and nonlinear relationship between data and expert knowledge, an Artificial Neural Network (ANN) is employed to develop a river health evaluation model. To validate the utility and effectiveness of the proposed method, we applied it with a case study in China. The proposed method yielded a Mean Absolute Error (MAE) of 4.78, while a multiple linear regression baseline model yielded an MAE of 8.653. This finding indicates that the assessment system can achieve significantly better comprehensiveness, robustness, accuracy, and applicability. • Present a more robust and accurate river health assessment method. • Combine objective data with the subjective expert knowledge in the system. • Utilize machine learning approaches in the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui Watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate gradient, gradient descent, and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from the LM-NN, and these results were then compared with those from PSO-based ANNs, including the conventional PSO neural network (CPSONN) and the improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. The results show that the PSO-based ANNs performed better than the LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing data set for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multiparameter (rainfall and water level) inputs, the RMSE of the testing data set for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN. [ABSTRACT FROM AUTHOR]

Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry have been developed from either forward or inverse modeling approaches. However, these models usually require extensive computer resources and lengthy computation. This paper discusses the use of the multi-layer perceptron (MLP) model, one of the artificial neural network (ANN) models widely adopted in engineering applications, to estimate the cooling load of a building. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing prestigious commercial building in Hong Kong that houses a mega complex and operates 24h a day. The paper also discusses the practical difficulties encountered in acquiring building-related data. In contrast to other studies that use ANN models to predict building cooling load, this paper includes the building occupancy rate as one of the input parameters used to determine building cooling load. The results demonstrate that the building occupancy rate plays a critical role in building cooling load prediction and significantly improves predictive accuracy. [Copyright &y& Elsevier]

Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry today have been developed from either forward or inverse modeling approaches. However, most of these models require extensive computer resources and involve lengthy computation. This paper discusses the use of data-driven intelligent approaches, a probabilistic entropy-based neural (PENN) model to predict the cooling load of a building. Although it is common knowledge that the presence and activity of building occupants have a significant impact on the required cooling load of buildings, practices currently adopted in modeling the presence and activity of people in buildings do not reflect the complexity of the impact occupants have on building cooling load. In contrast to previous artificial neural network (ANN) models, most of which employ a fixed schedule or historic load data to represent building occupancy in simulating building cooling load, this paper introduces two input parameters, dynamic occupancy area and rate and uses it to mimic building cooling load. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing grade A mega office buildings in Hong Kong with tenants including many multi-national financial companies that require 24-h air conditioning seven days a week. The dynamic changes that occur in the occupancy of these buildings therefore make it very difficult to forecast building cooling load by means of a fixed time schedule. The performance of simulation results demonstrate that building occupancy data play a critical role in building cooling load prediction and that their use significantly improves the predictive accuracy of cooling load models. [Copyright &y& Elsevier]

Abstract: The suitability of the project delivery system (PDS) selected for a project greatly influences the efficiency to conduct a project. It is not an easy task to select an appropriate PDS as a large amount of ambiguous information exists. The paper aims to develop a PDS selection model to help owners to make a decision. The similar projects are identified through the similarity metrics between the target project to be predicted and those in the database. In addition, some of the indicator values are examined and modified through DEA-BND model, and then they are trained by ANN model to predict an appropriate PDS for the target project. A survey was conducted by postal questionnaire to empirically validate the reliability of the model in China. The indicator system for the selection of an appropriate PDS is established. Through the comparison of results predicted by different models, it is found that the PDS selection model developed in this paper can predict PDS precisely, and shows higher reliability than the ANN model. A PDS selection model is developed by inputting project-specific data, which proves to be more accurate and less dependent on experts’ judgment. Its practical application will benefit the owner’s decision making in the selection of the PDS. [ABSTRACT FROM AUTHOR]

The fraction of absorbed photosynthetically active radiation (FPAR) is a key biophysiological parameter of terrestrial ecosystems. However, due to a lack of data with adequate spatial resolution and in long enough time series, there have been limitations in exploring the spatiotemporal changes of vegetation and response to climate change. In this study, a 1 km spatial resolution and 8-day period length dataset (FPARANN) was developed covering the years 1980 to 2018 and evaluated on spatiotemporal change consistency by validating with Gross Primary Production (GPP) observations from the Chinese Flux Observation and Research Network (ChinaFLUX), and comparison with other FPAR products. FPARANN provided a comparable performance in capturing seasonal change observed through GPP, according to the coefficient of determination (R2): 0.50, 0.51, 0.70 and 0.74 averaged for all sites, forest sites, grassland sites and cropland flux sites, respectively. The new data had more spatial similarity to the MODIS FPAR product (FPARMCD15A2) with a greater R2 (0.77) and a lower RMSE (0.12) than other products. With a newly developed dataset, combined with FPARANN (1980–2003) and FPARMCD15A2 (2004–2018), an overall increasing trend in FPAR was found for over 81% of the vegetated area of China from 1980 to 2018. FPAR increased more rapidly for over 83.7% of the area in the 2010s, and at a slower pace for over 62.1% of the area in the early 2000s, which was attributed to a decadal shifting of climate change. This study implies the new dataset is useful in quantifying vegetation changes and would be an important data source for future study of the carbon cycle, soil erosion, or evapotranspiration, with great application potential. [ABSTRACT FROM AUTHOR]

With the increasing demands for higher treatment efficiency, better effluent quality, and energy conservation in Urban Wastewater Treatment Plants (WWTPs), research has already been conducted to construct an optimized control system for Anaerobic-Anoxic-Oxic (AAO) process using a data-driven approach. However, existing data-driven optimization control systems for AAO mainly focus on improving effluent water quality and reducing energy consumption, therefore they lack consideration for the stability of bioreactors. Meanwhile, safety in the optimization control process is still missing, resulting in a lack of reliability in practical applications. In this study, long short-term memory based model-predictive control (LSTM-MPC) with safety verificationis developed for the real-time control of AAO. It is used to optimize the control of aeration volume, internal recirculation, and sludge internal recycle processes for both saving energy and maintaining the stability of the bioreactor operation. To ensure the safety of the control process, this study proposes three rationality verification methods based on historical operation experience. These methods are validated through data from a real-world WWTP in eastern China. The results show that the prediction model of LSTM-MPC is capable of accurately predicting the water quality variables of the AAO system, with mean square error (MSE) close to 2.64 and Nash–Sutcliffe model efficiency coefficient (NSE) of 0.99 on the validation dataset. The combination of LSTM-MPC and rationality verification achieves a stable control trajectory with a 7% reduction in oxygen usage compared to a conventional controller, demonstrating its efficacy as a safe and reliable control strategy for WWTPs. [ABSTRACT FROM AUTHOR]

Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China. [ABSTRACT FROM AUTHOR]

The vertical tunneling method is an emerging technique to build sewage inlets or outlets in constructed horizontal tunnels. The jacking force used to drive the standpipes upward is an essential factor during the construction process. This study aims to predict the jacking forces during the vertical tunneling construction process through two intelligence systems, namely, artificial neural networks (ANNs) and hybrid genetic algorithm optimized ANNs (GA-ANNs). In this paper, the Beihai hydraulic tunnel constructed by the vertical tunneling method in China is introduced, and the direct shear tests have been conducted. A database composed of 546 datasets with ten inputs and one output was prepared. The effective parameters are classified into three categories, including tunnel geometry factors, the geological factor, and jacking operation factors. These factors are considered as input parameters. The tunnel geometry factors include the jacking distance, the thickness of overlaying soil, and the height of overlaying water; the geological factor refers to the geological conditions; and the jacking operation factors consist of the dead weight of standpipes, effective overburden soil pressure, effective lateral soil pressure, average jacking speed, construction hours, and soil weakening measure. The output parameter, on the other hand, refers to the jacking force. Performance indices, including the coefficient of determination (R2), root mean square error (RMSE), and the absolute value of relative error (RE), are computed to compare the performance of the ANN models and the GA-ANN models. Comparison results show that the GA-ANN models perform better than the ANN model, especially on the RMSE values. Finally, parametric sensitivity analysis between the input parameters and output parameter is conducted, reaching the result that the height of overlaying water, the average jacking speed, and the geological condition are the most effective input parameters on the jacking force in this study. [ABSTRACT FROM AUTHOR]

Water supply systems that use rivers with high sedimentation levels may experience issues such as reservoir siltation. The suspended sediment concentration (SSC) of rivers experiences interannual variation and high nonlinearity due to its close relationship with meteorological factors, which increase the mismatch between the river water source and urban water demand. The raw water system scheduling problem is expressed as a reservoir and pump station control problem that involves real-time SSC changes. To lower the SSC of the water intake and lower the pumping station's energy consumption, a deep reinforcement learning (DRL) model based on SSC prediction was developed. The framework consists of a DRL model, a hydraulic model for simulating the raw water system, and a neural network for predicting river SSC. The framework was tested using data from a Yellow River water withdrawal pumping station in China with an average capacity of 400,000 m3/d. The strategy created in this study can reduce the system energy consumption per unit of water withdrawal by 8.33% and the average annual water withdrawal SSC by 37.01%, when compared to manual strategy. Meanwhile, the deep reinforcement learning algorithm had good response robustness to uncertain imperfect predictive data. [ABSTRACT FROM AUTHOR]

The uncertainty from the under-estimation and over-estimation of forest aboveground biomass (AGB) is an urgent problem in optical remote sensing estimation. In order to more accurately estimate the AGB of Pinus densata forests in Shangri-La City, we mainly discuss three non-parametric models—the artificial neural network (ANN), random forests (RFs), and the quantile regression neural network (QRNN) based on 146 sample plots and Sentinel-2 images in Shangri-La City, China. Moreover, we selected the corresponding optical quartile models with the lowest mean error at each AGB segment to combine as the best QRNN (QRNNb). The results showed that: (1) for the whole biomass segment, the QRNNb has the best fitting performance compared with the ANN and RFs, the ANN has the lowest R2 (0.602) and the highest RMSE (48.180 Mg/ha), and the difference between the QRNNb and RFs is not apparent. (2) For the different biomass segments, the QRNNb has a better performance. Especially when AGB is lower than 40 Mg/ha, the QRNNb has the highest R2 of 0.961 and the lowest RMSE of 1.733 (Mg/ha). Meanwhile, when AGB is larger than 160 Mg/ha, the QRNNb has the highest R2 of 0.867 and the lowest RMSE of 18.203 Mg/ha. This indicates that the QRNNb is more robust and can improve the over-estimation and under-estimation in AGB estimation. This means that the QRNNb combined with the optimal quantile model of each biomass segment provides a method with more potential for reducing the uncertainties in AGB estimation using optical remote sensing images. [ABSTRACT FROM AUTHOR]

Taking the Huaidian Sluice on the Shaying River in China as an example, this paper establishes the calculation model of the free flow based on artificial neural network and regression analysis. Four forms of discharge coefficient calculation equations were obtained by regression analysis, and three neural network models were established. The model is fully verified by using the measured data. The experimental results show that the third-order polynomial and multilayer perceptron neural network have better adaptability. The advantages and disadvantages of the different methods are analyzed and the cause of the error is identified. It provides a theoretical basis for dealing with the discharge calculation of small and medium dam. • Applying traditional regression methods and artificial neural networks to the calculation of sluice free outflows is an important innovation. • This paper establishes a multivariate and multi-combined input data set, which no one has tried. • By comparing the results of the two methods, we analyze the difference of the results in principle and find out the rules. It provides a new entry point for other scholars to study this. [ABSTRACT FROM AUTHOR]

The rainfall-induced landslide early warning model (LEWM) is an important means to mitigate property loss and casualties, but the conventional discriminant matrix-based LEWM (DLEWM) leaves room for subjectivity and limits warning accuracy. Additionally, it is important to employ appropriate indicators to evaluate warning model performance. In this study, a new method for calculating the spatiotemporal probability of rainfall-induced landslides based on a Bayesian approach is proposed, and a probabilistic-based LEWM (PLEWM) at the regional scale is developed. The method involves four steps: landslide spatial probability modeling, landslide temporal probability modeling, coupling of spatial and temporal probability models, and the conversion method from the spatiotemporal probability index to warning levels. Each step follows the law of probability and is tested with real data. At the same time, we propose the idea of using economic indicators to evaluate the performance of the multilevel LEWM and reflect its significant and unique aspects. The proposed PLEWM and the conventional DLEWM are used to conduct simulate warnings for the study area day-by-day in the rainy season (July-September) from 2016 to 2020. The results show that the areas of the 2nd-, 3rd-, and 4th-level warning zones issued by the PLEWM account for 60.23%, 45.99%, and 43.98% of those of the DLEWM, respectively. The investment in issuing warning information and the losses caused by landslides account for 54.54% and 59.06% of those of the DLEWM, respectively. Moreover, under extreme rainfall conditions, the correct warning rate of the PLEWM is much higher than that of the DLEWM. [ABSTRACT FROM AUTHOR]

An energy storage operation chart (ESOC) is one of the most popular methods for conventional cascade reservoir operation. However, the problem of distributing the total output obtained from the ESOC has not yet been reasonably solved. The discriminant coefficient method is a traditional method for guiding the output distribution by determining the order of reservoir supply or storage; however, it cannot quantify the water used in operation. Thus, this study develops a new output distribution model using a polynomial fitting method and an artificial neural network to express the functional relationship derived from the deterministic optimization results of long-term runoff series to maximize power generation. Cascade reservoirs of the lower reaches of the Jinsha River in China were selected for the case study. Compared to the discriminant coefficient method, the proposed method can rationally distribute the total output, thus avoiding the problem of concentrated deserting water in downstream reservoirs that occurs in the discriminant coefficient method. In general, this study proposes an effective alternative method to guide cascade reservoir operation. [ABSTRACT FROM AUTHOR]

Trophic Level Index (TLI) is often used to assess the general eutrophication state of inland lakes in water science, technology, and engineering. In this paper, a data-driven inland-lake eutrophication assessment method was proposed by using an artificial neural network (ANN) to build relationships from remote sensing data and in-situ TLI sampling. In order to train the net, Moderate Resolution Imaging Spectroradiometer (MODIS, which has a revisit cycle of 4 times per day) data were combined with in-situ observations. Results demonstrate that the TLI obtained directly from remote-sensing images using the data-driven method is more accurate than the TLI calculated from the water quality factors retrieved from remote-sensing images using a multivariate regression method. Spatially continuous and quasi-real time results were retrieved by using MODIS data. This method provides an efficient way to map the TLI spatial distribution in inland lakes, and provides a scheme for increased automation in TLI estimation. [ABSTRACT FROM AUTHOR]

As one of the emerging digital technologies, building information modeling (BIM) has been increasingly used in the architecture, engineering, and construction (AEC) industry of many countries. One of the core benefits associated with BIM is facilitating collaboration among project teams, thereby improving information sharing and project performance by providing BIM-based construction networks (BbCNs). Nevertheless, it is still challenging to ensure an effective collaboration process in BIM-enabled projects. Several studies have investigated the factors that influence collaboration in BIM-enabled projects; however, most of these studies focus on technical matters of BIM, while the perspectives of socio-organization and processes have received less attention and are still in the conceptual stage, especially for the role of people management. Thus, an exploratory study was proposed to explore how key factors influence the professionals' willingness to collaborate in BIM-enabled projects. A total of 10 hypotheses were established based on literature review and expert verification. Following that, a questionnaire survey was administered in China to solicit the opinions of 273 BIM professionals for data collection. The hypotheses were then examined on the basis of the predictive capacity of regression analysis (binary logistic regression), with the findings validated using neural network analysis (multilayer perceptron). It was found that seven independent variables have a statistically significant influence on professionals' willingness to collaborate in BIM-enabled projects, namely, professional knowledge, skill, training, investment, BIM tools, BIM ownership, and standards and regulations. Among them, the variable of professional knowledge was ranked as the most influential factor. While extending the existing knowledge of literature, the findings of this study also deliver insights for stakeholders by enhancing their understanding of the BIM collaboration process and its influence factors. [ABSTRACT FROM AUTHOR]

Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations. [ABSTRACT FROM AUTHOR]

Owing to the complexity of the climate system and limitations of numerical dynamical models, machine learning based on big data has been used for climate forecasting in recent years. In this study, we attempted to use an artificial neural network (ANN) for summer precipitation forecasts in the Yangtze-Huaihe River Basin (YHRB), eastern China. The major ANN employed here is the standard backpropagation neural network (BPNN), which was modified for application to the YHRB. Using the analysis data of precipitation and the predictors/factors of atmospheric circulation and sea surface temperature, we calculated the correlation coefficients between precipitation and the factors. In addition, we sorted the top six factors for precipitation forecasts. In order to obtain accurate forecasts, month (factor)-to-month (precipitation) forecast models were applied over the training and validation periods (i.e., summer months over 1979–2011 and 2012–2019, respectively). We compared the standard BPNN with the BPNN using a genetic algorithm-based backpropagation (GABP), support vector machine (SVM) and multiple linear regression (MLR) for the summer precipitation forecast after the model training period, and found that the GABP method is the best among the above methods for precipitation forecasting, with a mean absolute percentage error (MAPE) of approximately 20% for the YHRB, which is substantially lower than the BPNN, SVM and MLR values. We then selected the best summer precipitation forecast of the GABP month-to-month models by summing up monthly precipitation, in order to obtain the summer scale forecast, which presents a very successful performance in terms of evaluation measures. For example, the basin-averaged MAPE and anomaly rate reach 4.7% and 88.3%, respectively, for the YHRB, which can be a good recommendation for future operational services. It appears that sea surface temperatures (SST) in some key areas dominate the factors for the forecasts. These results indicate the potential of applying GABP to summer precipitation forecasts in the YHRB. [ABSTRACT FROM AUTHOR]

With the deployment of 5G Internet of Things (IoT) in the power system, the efficiency of smart grid is improved by increasing two-way interactions in different layers in smart grid. However, it introduces more attack interfaces that the traditional information security system in smart grid cannot response in time. The neuroscience-inspired models have shown their effectiveness in solving security and optimization problems in smart grid. How to improve the security mechanism in smart grid while taking into account the optimization of data transmission efficiency using neuroscience-inspired algorithms is the problem to be solved in this study. Therefore, an information security system based on artificial neural network (ANN) and improved multiple protection model is proposed. Based on the ANN algorithm, the link state sample space is used to train the model to obtain the optimal transmission path in 5G power communication network. Integrating the intelligent link state module, the zero-trust security protection platform using case-based learning algorithm is designed and taken as the first protection, the network security logical isolation facility is taken as the second protection, and the forward and backward isolation facilities are set as the third protection to achieve the strengthened security of 5G IoT in smart grid. The experimental results show the efficiency and effectiveness of the proposed algorithms. In addition, the experimental results also show that the proposed system can resist malicious terminal access, terminal hijacking, data tampering and eavesdropping, protocol fuzzy, and denial-of-service attacks, so as to reduce the security risks of 5G IoT in smart grid. Since the proposed system can be easily integrated into the existing smart grid structure in China, the proposed system can provide a reference for the design and implementation of 5G IoT in smart grid. [ABSTRACT FROM AUTHOR]

• PSQRNN is proposed for the regional electricity consumption forecasting, which combines panel data, semiparametric model and QRNN (quantile regression neural networks). • PSQRNN is a new framework exploring the potential linear and nonlinear relationship and maintaining a better interpretability of parametric models. • PSQRNN is trained by assembling the penalized quantile regression with LASSO, ridge regression and backpropagation algorithm. Addressing the forecasting issues is one of the core objectives of developing and restructuring of electric power industry in China. However, there are not enough efforts that have been made to develop an accurate electricity consumption forecasting procedure. In this paper, a panel semiparametric quantile regression neural network (PSQRNN) is developed by combining an artificial neural network and semiparametric quantile regression for panel data. By embedding penalized quantile regression with least absolute shrinkage and selection operator (LASSO), ridge regression and backpropagation, PSQRNN keeps the flexibility of nonparametric models and the interpretability of parametric models simultaneously. The prediction accuracy is evaluated based on China's electricity consumption data set, and the results indicate that PSQRNN performs better compared with three benchmark methods including BP neural network (BP), Support Vector Machine (SVM) and Quantile Regression Neural Network (QRNN). [ABSTRACT FROM AUTHOR]

The roots of Salvia miltiorrhiza Bunge. are commonly used in the treatment of cardiovascular diseases, and tanshinones and salvianolic acids are its main active ingredients. However, the composition and content of active ingredients of S. miltiorrhiza planted in different regions of the soil environment are also quite different, which adds new difficulties to the large-scale and standardization of artificial cultivation. Therefore, in this study, we measured the active ingredients in the roots of S. miltiorrhiza and the contents of rhizosphere soil elements from 25 production areas in eight provinces in China, and used the data to develop a prediction model based on BP (back propagation) neural network. The results showed that the active ingredients had different degrees of correlation with soil macronutrients and trace elements, the prediction model had the best performance (MSED0.0203, 0.0164; R2 D0.93, 0.94). The artificial neural network model was shown to be a method that can be used to screen the suitable cultivation sites and proper fertilization. It can also be used to optimize the fertilizer application at specific sites. It also suggested that soil testing formula fertilization should be carried out for medicinal plants like S. miltiorrhiza, which is grown in multiple origins, rather than promoting the use of "special fertilizer'' on a large scale. Therefore, the model is helpful for efficient, rational, and scientific guidance of fertilization management in the cultivation of S. miltiorrhiza. [ABSTRACT FROM AUTHOR]

Artificial neural network is a powerful tool for modelling and prediction in various fields. Tobacco management specialists need simple and accurate techniques to evaluate tobacco growth and cultivation in the growth period. The objective in this study is to develop a back-propagation neural network (NN) model to accurately predict tobacco growth under various climatic and soil conditions in China. The proposed model used the field test data at 11 locations in main tobacco areas in China. As the output of the NN, the dry weight of tobacco plants was significantly influenced by dry weigh preceding stage, time for sampling, average temperature, relative humidity, soil organic matter and available P value, so these 6 environment factors were taken as the NN input. In the study, an optimal NN model was developed with 6 inputs, 1 output and 19 neurons in the hidden layer. With the same test data, the proposed NN model could perform better than the conventional regression model in terms of the relative error ranges and relative coefficient. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes the use of a coupled fault tree analysis (FTA) and artificial neural network (ANN) model to improve the prediction of the potential risk of coal and gas outburst events during the underground mining of thick and deep Chinese coal seams. The model developed has been used to investigate the gas emission characteristics and the geological conditions that exist within the Huaibei coal mining region, Anhui province, China. The coal seams in this region exhibit a high incidence of coal and gas outbursts. An analysis of the results obtained from an initial application of an FTA model, identified eight dominant model parameters related to the gas content or geological conditions of the coal seams, which characterize the potential risk of in situ coal and gas outbursts. The eight dominant model parameters identified by the FTA method were subsequently used as input variables to an ANN model. The results produced by the ANN model were used to develop a qualitative risk index to characterize the potential risk level of occurrence of coal and gas outburst events. Four different potential risk alarm levels were defined: SAFE, POTENTIAL, HIGH and STRONG. Solutions to the prediction model were obtained using a combination of quantitative and qualitative data including the gas content or gas pressure and the geological and geotechnical conditions of coal seams. The application of this combined solution method identified more explicit and accurate model relationships between the in situ geological conditions and the potential risk of coal and gas outbursts. An analysis of the model solutions concluded that the coupled FTA and ANN model may offer a reliable alternative method to forecast the potential risk of coal and gas outbursts. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, support vector machine (SVM) is used to predict hourly building cooling load. The hourly building cooling load prediction model based on SVM has been established, and applied to an office building in Guangzhou, China. The simulation results demonstrate that the SVM method can achieve better accuracy and generalization than the traditional back-propagation (BP) neural network model, and it is effective for building cooling load prediction. [Copyright &y& Elsevier]

Summary: In this paper, three data-preprocessing techniques, moving average (MA), singular spectrum analysis (SSA), and wavelet multi-resolution analysis (WMRA), were coupled with artificial neural network (ANN) to improve the estimate of daily flows. Six models, including the original ANN model without data preprocessing, were set up and evaluated. Five new models were ANN-MA, ANN-SSA1, ANN-SSA2, ANN-WMRA1, and ANN-WMRA2. The ANN-MA was derived from the raw ANN model combined with the MA. The ANN-SSA1, ANN-SSA2, ANN-WMRA1 and ANN-WMRA2 were generated by using the original ANN model coupled with SSA and WMRA in terms of two different means. Two daily flow series from different watersheds in China (Lushui and Daning) were used in six models for three prediction horizons (i.e., 1-, 2-, and 3-day-ahead forecast). The poor performance on ANN forecast models was mainly due to the existence of the lagged prediction. The ANN-MA, among six models, performed best and eradicated the lag effect. The performances from the ANN-SSA1 and ANN-SSA2 were similar, and the performances from the ANN-WMRA1 and ANN-WMRA2 were also similar. However, the models based on the SSA presented better performance than the models based on the WMRA at all forecast horizons, which meant that the SSA is more effective than the WMRA in improving the ANN performance in the current study. Based on an overall consideration including the model performance and the complexity of modeling, the ANN-MA model was optimal, then the ANN model coupled with SSA, and finally the ANN model coupled with WMRA. [Copyright &y& Elsevier]

Abstract: This paper applies artificial neural network to predict hourly air pollutant concentrations near an arterial in Guangzhou, China. Factors that influence pollutant concentrations are classified into four categories: traffic-related, background concentration, meteorological and geographical. The hourly averages of these influential factors and concentrations of carbon monoxide, nitrogen dioxide, particular matter and ozone were measured at three selected sites near the arterial using vehicular automatic monitoring equipments. Models based on back-propagation neural network were trained, validated and tested using the collected data. It is demonstrated that the models are able to produce accurate prediction of hourly concentrations of the pollutants respectively more than 10h in advance. A comparison study shows that the neural network models outperform multiple linear regression models and the California line source dispersion model. [Copyright &y& Elsevier]