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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]

In this paper, we will put forward a hybrid model to predict the tendency of the fund, which is based on neural network combined with Genetic Algorithm (GA), Optimal Entropy (OE) and Variation Function. In China, financial market has great potential, but due to the intensity instability of the fund market. At present, the forecast about the trend of funds is still a not very accuracy. This model includes data pretreatment, input and output. Data preprocessing part is the time and characteristic alignment. Input part is that according to different characteristics, we use OE, GA and change function as of the input of neural network. Output part is using back propagation algorithm to get the final forecast. Because the input data has many different characteristics, in order to make full use of these characteristics, using the OE and GA of the global optimal feature selection can make the input model is more superior, which will help to improve the prediction accuracy of the proposed model. The experimental results show that using this hybrid model predicts china fund net value, the accuracy can reach 97.8%. This result is more accuracy when compare with China's existing institutions and financial website is more accurate. [ABSTRACT FROM AUTHOR]

Recently security issues like investment and financing in China's power industry have become increasingly prominent, bringing serious challenges to the financial security of the domestic power industry. Thus, it deserves to develop financial safety evaluation towards the Chinese power industry and is of practical significance. In this paper, the GA (genetic algorithm) is used to optimize the connection weights and thresholds of the traditional BPNN (back propagation neural network) so the new model of BPNN based on GA is established, hereinafter referred to as GA-BPNN (back propagation neural network based on genetic algorithm). Then, an empirical example of the electric power industry in China during the period 2003–2010 was selected to verify the proposed algorithm. By comparison with three other algorithms, the results indicate the model can be applied to evaluate the financial security of China's power industry effectively. Then index values of the financial security of China's power industry in 2011 were obtained according to the tested prediction model and the comprehensive safety scores and grades are calculated by the weighted algorithm. Finally, we analyzed the reasons and throw out suggestions based on the results. The work of this paper will provide a reference for the financial security evaluation of the energy industry in the future. [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]

In this paper, the artificial neural network (ANN) model was used to evaluate the degree of intensive urban land use in Nanjing City, China. The construction and application of the ANN model took into account the comprehensive, spatial and complex nature of urban land use. Through a preliminary calculation of the degree of intensive land use of the sample area, representative sample area selection and using the back propagation neural network model to train, the intensive land use level of each evaluation unit is finally determined in the study area. Results show that the method can effectively correct the errors caused by the limitations of the model itself and the determination of the ideal value and weights when the multifactor comprehensive evaluation is used alone. The ANN model can make the evaluation results more objective and practical. The evaluation results show a tendency of decreasing land use intensity from the core urban area to the periphery and the industrial functional area has relatively low land use intensity compared with other functional areas. Based on the evaluation results, some suggestions are put forward, such as transforming the mode of urban spatial expansion, strengthening the integration and potential exploitation of the land in the urban built-up area, and strengthening the control of the construction intensity of protected areas. [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]

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]

Primary fine particulate matters with a diameter of less than 10µm(PM10) are important air emissions causing human health damage. PM10concentration forecast is important and necessary to perform in order to assess the impact of air on the health of living beings. To better understand the PM10pollution health risk in Taiyuan City, China, this paper forecasted the temporal and spatial distribution of PM10yearly average concentration, using Back Propagation Artificial Neural Network (BPANN) model with various air quality parameters. The predicted results of the models were consistent with the observations with a correlation coefficient of 0.72. The PM10yearly average concentrations combined with the population data from 2002 to 2008 were given into the Intake Fraction (IF) model to calculate the IFs, which are defined as the integrated incremental intake of a pollutant released from a source category or a region over all exposed individuals. The results in this study are only for main stationary sources of the research area, and the traffic sources have not been included. The computed IFs results are therefore under-estimations. The IFs of PM10from Taiyuan with a mean of 8.5 per million were relatively high compared with other IFs of the United States, Northern Europe and other cities in China. The results of this study indicate that the artificial neural network is an effective method for PM10pollution modeling, and the Intake Fraction model provides a rapid population risk estimate for pollutant emission reduction strategies and policies. [ABSTRACT FROM AUTHOR]

Abstract: Wind energy, which is intermittent by nature, can have a significant impact on power grid security, power system operation, and market economics, especially in areas with a high level of wind power penetration. Wind speed forecasting has been a vital part of wind farm planning and the operational planning of power grids with the aim of reducing greenhouse gas emissions. Improving the accuracy of wind speed forecasting algorithms has significant technological and economic impacts on these activities, and significant research efforts have addressed this aim recently. However, there is no single best forecasting algorithm that can be applied to any wind farm due to the fact that wind speed patterns can be very different between wind farms and are usually influenced by many factors that are location-specific and difficult to control. In this paper, we propose a new hybrid wind speed forecasting method based on a back-propagation (BP) neural network and the idea of eliminating seasonal effects from actual wind speed datasets using seasonal exponential adjustment. This method can forecast the daily average wind speed one year ahead with lower mean absolute errors compared to figures obtained without adjustment, as demonstrated by a case study conducted using a wind speed dataset collected from the Minqin area in China from 2001 to 2006. [Copyright &y& Elsevier]

Abstract: Suitability evaluation of urban construction land based on geo-environmental factors is the process of determining the fitness of a given tract of land for construction. This process involves a consideration of the geomorphology, geology, engineering geology, geological hazards, and other geological factors and is the basis of urban construction land planning and management. With the support of Geographic Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups comprising nine separate subfactors of geo-environmental attributes were selected to be used in the evaluation of the suitability level for construction land in Hangzhou. This was based on K-means clustering and back-propagation (BP) neural network methods due to their advantages in fast computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results based on K-means clustering and BP neural network were compared and analyzed, and the accuracy evaluation was set. The results showed that the geo-environmental suitability evaluation results of construction land based on K-means clustering and BP neural network were similar in terms of the distribution and scale of construction land suitability level. At the same time, the results of the two evaluation methods were consistent with the variability in suitability level, engineering geology, and hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout. The suitability level of the urban construction land based on the geo-environment in Hangzhou was divided into four construction sites: land for building super high-rise and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and nonbuilding land. The results of the suitability evaluation for each category will provide a scientific basis for decision-making in urban development in Hangzhou. [Copyright &y& Elsevier]

Abstract: Nitrate nitrogen (NO3 −-N) from agricultural activities has become the main source of groundwater pollution. A spatial distribution pattern of groundwater NO3 −-N pollution is vital for agricultural ecological and environmental management. The objective of this paper is to investigate the potential of artificial neural network to explore the spatial distribution of groundwater NO3 −-N pollution in Shandong intensive farming regions of China. A detailed field campaign has been carried out to obtain the 216 sample site data focusing on the intensive farming regions in Shandong province. Considering the practical difficulty of the complex nonlinear relationship between multi-factors and groundwater nitrate, a Back Propagation Neural Network (BPNN) was developed for modeling groundwater NO3 −-N concentration. In order to perform the analysis, both natural and anthropogenic factors have been studied, such as soil characteristics, fertilizer usage and terrain factors and so on. Finally, soil organic matter content, total nitrogen content and nitrogen fertilizer data were chosen as input features of the BPNN for having the best correlation with groundwater NO3 −-N concentration. The results indicated that areas with higher NO3 −-N concentration in groundwater are mainly located in the region of excessive use of nitrogen fertilizer and low groundwater runoff modulus. The application results suggested that the BPNN provide a promising approach for analyzing the spatial variability of the groundwater NO3 −-N concentration. [Copyright &y& Elsevier]

Abstract: Under limited resources in corporation education training, to enhance human resources quality, making education training program planning more efficient is a significant issue in training future talents. In accordance with Taiwan TrainQuali System (TTQS), the basic training structure is ton specify P (Plan) and D (Design). Ensuing results will be easier and successful. From TTQS database of Bureau of Employment and Vocational Training, corporations in Taoyuan, Hsinchu and Miaoli winning Gold Medals (Group B) have gaps outside control line in P and D. Enhancement is needed in the gap. The paper aims at a certain company winning Gold Medals in Taoyuan, Hsinchu and Miaoli to locate hidden or unobvious information with data mining, which will help future education training course planning and design. The researchers use two-stage clustering (SOM and K-means) under data mining theory to collect personnel training data of Automobile Corporation A in Taiwan and China with data mining and analysis. The results under the two algorithms will serve as reference for future education training courses. In the end, in combination of back-propagation neural network to develop education training prediction model, the research offers reference for writing knowledge management system to enhance effects of personnel participation in training at corporations. [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]

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]

• An evaluation model of soil quality in arid area BP - ANN based was established. • The model combined heavy metals and nutrients in soil has high accuracy. • Environmental controlling factors of soil quality have regional differences. Soil quality evaluation is an effective way to improve ecological environment quality of soil and to perfect management system as well as keep its productivity of the soil sustainable. However, in arid areas, the comprehensive study of soil quality has been paid little attention. This paper aims to classify and evaluate comprehensive soil quality which combined heavy metal contamination with soil nutrients in the arid area, and to explore the natural environmental controlling factors of soil quality under regional differences. Taking Hexi area, a typical arid area in northwest China, as the study area, the comprehensive evaluation model of soil quality based on back propagation artificial neural network (BP - ANN) was proposed. The results showed that the accuracies of the validated models were both more than 97 %. The soil quality in Hexi area was divided into six classes: IA, IB, IC, IIA, IIB, IIC (The number (I and II) represents the degree of heavy metal pollution, the larger the number, the higher the pollution degree; the letter (A, B, and C) represents the nutrient level, the latter the letter, the more barren the nutrient). Most of the samples were classified as IB and IC, indicating that the soil quality was good with mild heavy metal contamination and poorer nutrients. Group analysis was used to explore the impact of natural environmental factors on soil quality under different environmental characteristics through linear regression modeling. The results showed that elevation, annual mean cumulative precipitation, slope and topographic wetness index were the environmental controlling factors of the soil quality for all samples. Besides, the other important factors affecting soil quality in group 1 were: annual mean temperature and soil type. For group 2 it was LUCC (Land Use Cover Change). This study is of great significance to classify and define the soil quality, to maintain and improve agricultural production and soil ecological health in arid areas. [ABSTRACT FROM AUTHOR]

• Random forest model is applied to performance prediction of GSHP system. • The superiority of the method is verified from multiple angles. • Robustness, interpretability, and efficiency are taken into account. • This study enriches database of predictive model in energy management systems. With rapid development of artificial intelligence, data-driven prediction models play an important role in energy prediction, fault detection, and diagnosis. This paper proposes an ensemble approach using random forest (RF) for hourly performance predictions of GSHP system. Two years of in situ data were collected in an educational building situated in severe cold area in China. Prediction models were established for performance indicators, and results indicate that the average error for COP s , COP u , EER s and EER u were all controlled within 5%. The model established by small amount of data can accurately predict long-term performance, thereby reducing time and difficulty of data collection. RF models, trained with different parameter settings were compared, results indicate that model accuracy was not very sensitive to variables numbers. The impact of input variables on prediction performance was analyzed, and importance ranking changed with period and performance indicators. By comparing the variable importance list, it was possible to establish which parameters were abnormal and lists of different periods can reflect whether the energy structure of building has changed. The overall superiority of RF was verified by comparing with back propagation neural network (BPNN) from robustness, interpretability, and efficiency. First, since GSHP system involving multiple indicators, the robustness, measured by average accuracy, was used to evaluate the accuracy level. According to CV-RMSE, robustness of RF is approximately 3.3% higher than that of BPNN. Second, RF is highly interpretive but BPNN is typical black box model. Finally, modeling complexity and training time of BPNN were much greater than RF. [ABSTRACT FROM AUTHOR]