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Open AccessArticle

Geographic Information System Technology Combined with Back Propagation Neural Network in Groundwater Quality Monitoring

School of Earth Sciences and Resources, China University of Geosciences, Beijing 100000, China
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ISPRS Int. J. Geo-Inf. 2020, 9(12), 736; https://doi.org/10.3390/ijgi9120736
Received: 14 October 2020 / Revised: 20 November 2020 / Accepted: 4 December 2020 / Published: 9 December 2020
(This article belongs to the Special Issue The Use of GIS and Soft Computing Methods in Water Resource Planning)
This study was conducted to explore the distribution and changes of groundwater resources in the research area, and to promote the application of geographic information system (GIS) technology and its deep learning methods in chemical type distribution and water quality prediction of groundwater. The Shiyang River Basin in Minqin County was selected as the research object for analyzing the natural components distribution and its preliminary forecast in partial areas. With the priority control of groundwater pollutants, the concentration changes of four indicators (including the permanganate index) in different spatial distributions were analyzed based on the GIS technology, so as to provide a basis for the groundwater quality prediction. Taking the permanganate as a benchmark, this study evaluated the prediction effects of the conventional back propagation (BP) neural network (BPNN) model and the optimized BPNN based on the golden section (GBPNN) and wavelet transform (WBPNN). The algorithm proposed in this study is compared with several classic prediction algorithms for analysis. Groundwater quality level and distribution rules in the research area are evaluated with the proposed algorithm and GIS technology. The results reveal that GIS technology can characterize the spatial concentration distribution of natural indicators and analyze the chemical distribution of groundwater quality based on it. In contrast, the WBPNN has the best prediction result. Its average error of the whole process is 3.66%, and the errors corresponding to the six predicated values are all below 10%, which is dramatically better than the values of the other two models. The maximal prediction accuracy of the proposed algorithm is 97.68%, with an average accuracy of 96.12%. The prediction results on the water quality level are consistent with the actual condition, and the spatial distribution rules of the groundwater water quality can be shown clearly with the GIS technology combined with the proposed algorithm. Therefore, it is of great significance to explore the distribution and changes of regional groundwater quality, and this studywill play a critical role in determining the groundwater quality. View Full-Text
Keywords: geographic information system; inverse distance weighted spatial interpolation; back propagation neural network; golden section; wavelet transform; permanganate index geographic information system; inverse distance weighted spatial interpolation; back propagation neural network; golden section; wavelet transform; permanganate index
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MDPI and ACS Style

Sun, J.; Wang, G. Geographic Information System Technology Combined with Back Propagation Neural Network in Groundwater Quality Monitoring. ISPRS Int. J. Geo-Inf. 2020, 9, 736. https://doi.org/10.3390/ijgi9120736

AMA Style

Sun J, Wang G. Geographic Information System Technology Combined with Back Propagation Neural Network in Groundwater Quality Monitoring. ISPRS International Journal of Geo-Information. 2020; 9(12):736. https://doi.org/10.3390/ijgi9120736

Chicago/Turabian Style

Sun, Jing; Wang, Genhou. 2020. "Geographic Information System Technology Combined with Back Propagation Neural Network in Groundwater Quality Monitoring" ISPRS Int. J. Geo-Inf. 9, no. 12: 736. https://doi.org/10.3390/ijgi9120736

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