Abstract
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.
Highlights
As the source of life, water is an extremely important resource in daily production and human life, as well as a critical environmental resource that ensures stable survival of various organisms on the earth [1,2]
geographic information system (GIS) technology was applied to the analysis of the spatial distribution of groundwater pollutants, and the BPNN method was applied to the monitoring of groundwater quality for better analyzing the groundwater quality in the downstream areas such as Shiyang River in Minqin County
This study analyzed the groundwater quality monitoring and spatial distribution through the combination of GIS technology and BPNN method; it was found that the golden section and wavelet transform can significantly improve the predictive performance of BPNN after the BPNN is optimized
Summary
As the source of life, water is an extremely important resource in daily production and human life, as well as a critical environmental resource that ensures stable survival of various organisms on the earth [1,2]. GIS technology was applied to the analysis of the spatial distribution of groundwater pollutants, and the BPNN method was applied to the monitoring of groundwater quality for better analyzing the groundwater quality in the downstream areas such as Shiyang River in Minqin County. Based on the water chemical characteristics and spatial distribution of the groundwater in the research area, the groundwater was predicted using the neural network method based on the deep learning to provide a reliable reference for determining the groundwater quality level. This study analyzed the groundwater quality monitoring and spatial distribution through the combination of GIS technology and BPNN method; it was found that the golden section and wavelet transform can significantly improve the predictive performance of BPNN after the BPNN is optimized.
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