Application of GA-BP Neural Network in Accurately Characterizing the Diffusion Range of Groundwater Pollutants in the Site

JI Jiayun; XIAO Xiao; YANG Pinlu; LIU Yang; ZHOU Yahong

doi:10.15898/j.ykcs.202409280204

Rock and Mineral Analysis > 2025 > In Press, Accepted Manuscript > DOI: 10.15898/j.ykcs.202409280204

JI Jiayun，XIAO Xiao，YANG Pinlu，et al. Application of GA-BP Neural Network in Accurately Characterizing the Diffusion Range of Groundwater Pollutants in the Site[J]. Rock and Mineral Analysis，2025，44（4）：1−14. DOI: 10.15898/j.ykcs.202409280204

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Application of GA-BP Neural Network in Accurately Characterizing the Diffusion Range of Groundwater Pollutants in the Site

JI Jiayun^{1, 2,},
XIAO Xiao^1,,
YANG Pinlu^3,,
LIU Yang^1,,
ZHOU Yahong^{1, 4, 5, ,}

1.
College of Water Resources and Environment, Hebei GEO University, Shijiazhuang 050031, China
2.
College of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang 050031, China
3.
Tangshan Water Supply Company Limited, Tangshan 063000, China
4.
Hebei Collaborative Innovation Center for Sustainable Utilization of Water Resources and Optimization of Industrial Structure, Shijiazhuang 050031, China
5.
Hebei Key Laboratory of Sustainable Utilization and Development of Water Resources, Shijiazhuang 050031, China

More Information

Received Date: September 27, 2024
Revised Date: January 02, 2025
Accepted Date: January 09, 2025
Available Online: February 21, 2025

Abstract

HIGHLIGHTS
(1) The distribution of groundwater Mn²⁺ concentration in the study area does not conform to the law of Mn²⁺ production and migration, and needs to be corrected.

(2) The GA-BP neural network was used to predict the concentration of Mn²⁺ in the area with less distribution of sampling points and correct the distribution of Mn²⁺.

(3) It is verified that the corrected Mn²⁺ concentration distribution conforms to the site Mn²⁺ production mechanism and groundwater dynamics conditions.

ABSTRACT

This study addresses the issue of unevenly distributed sampling points, which leads to inaccurate characterization of pollutant diffusion ranges. Using ArcGIS spatial interpolation, the distribution of Mn²⁺ ions in a chemical park was analyzed, revealing discrepancies due to uneven sampling. To overcome this, two neural network models—GA-BP and standard BP—were applied to predict Mn²⁺ concentrations at unsampled locations. The GA-BP neural network, optimized with a Genetic Algorithm, showed the best performance, filling gaps in data and allowing for a more accurate concentration distribution map. This revised map was used to delineate the Mn²⁺ diffusion range, which was further validated with the known production and migration mechanisms of Mn²⁺. The results demonstrate that the GA-BP model significantly improves the accuracy of pollutant diffusion mapping and offers a more reliable method for environmental pollution assessment, especially in areas with limited sampling data.
- groundwater,
- chemical industrial park,
- GA-BP neural network,
- influence range
BRIEF REPORT
Significance: Groundwater pollution is a common environmental issue in industrial areas，in which heavy metal pollution (e.g.，Mn²⁺，chromium，lead，iron，etc.) poses a serious threat to ecosystems and human health. The Technical Guidelines for Identification and Assessment of Ecological Environmental Damage Environmental Elements Part 1: Soil and Groundwater (GB/T 39792.1—2020) released in 2021 sets more stringent standards for the accuracy of characterizing groundwater pollutant diffusion. However，due to site constraints limiting groundwater monitoring points and the uneven distribution of sampling locations，traditional spatial interpolation methods (such as the Kriging method，inverse distance weighting，and spline functions) introduce significant errors in predicting pollutant diffusion，making it difficult to accurately capture their migration patterns.
Methods: Using Mn²⁺ pollution in a chemical industry park as a case study，this research utilizes ArcGIS spatial interpolation analysis to reveal that there is a large deviation in the distribution trend of Mn²⁺ concentration and its formation mechanism，and explores multiple interpolation methods for correction，yet the results still do not meet the required accuracy. In view of this，in this paper，the back propagation neural network (GA-BP) optimized by genetic algorithm is compared with the standard back propagation neural network (BP) to optimize the prediction of pollutant concentration and improve the characterization accuracy of the diffusion range.
　　First，a spatial interpolation of Mn²⁺ concentrations using GIS interpolation methods (Kriging method，inverse distance weighting，spline functions，etc.) shows that the predictions of these methods do not correspond to the direction of groundwater in the study area and to the mechanism of Mn²⁺ formation，indicating the low applicability of traditional interpolation methods when monitoring points are unevenly distributed. Therefore，this paper further uses GA-BP neural network for concentration prediction，chooses the model with the best fit for Mn²⁺ concentration prediction at unmonitored points. In combination with ArcGIS spatial interpolation，we delimit the diffusion range of Mn²⁺ and verify it in accordance with the mechanism of Mn²⁺ production.
　　In this study，twelve groundwater monitoring sites were deployed in the chemical industry park，and the data were divided into training sets and test sets. The training set includes points upstream，contamination plume and downstream of pollution sources to ensure that the model can learn pollution migration characteristics under different hydrodynamic conditions. In the test set，three monitoring points were selected，located in the contamination plume and downstream，to test the prediction ability of the model.
Data and Results: This study first models the relationship between Mn²⁺ concentration and spatial distribution using a standard BP neural network，then applies a genetic algorithm (GA) to optimize its weights and thresholds for improved prediction accuracy. After training，an optimized GA-BP neural network is used to predict the concentration of Mn²⁺ in an unmonitored area and to optimize the delineation of the extent of pollutant dispersion. Finally，model reliability is validated through comparison with measured data，considering Mn²⁺ migration mechanisms and groundwater dynamics. GA-BP neural networks perform best in the prediction of Mn²⁺ concentration，with a prediction error close to 0 and a higher fitting accuracy than standard BP neural networks. Using a GA-BP neural network to supplement the Mn²⁺ concentration data for the missing regions of the monitoring points and to replot the Mn²⁺ concentration distribution，the result shows that: the dispersion of Mn²⁺ in the centre of the chemical park is 1.16×10⁶ m²，of which 2.13×10⁵ m² goes beyond the chemical park. A comparison of the extent of pollutant dispersion before and after optimization demonstrates that the revised Mn²⁺ extent aligns more closely with its generation mechanism and migration dynamics. That is，the direction of Mn²⁺ migration is influenced jointly by the degradation of organic matter and the flow of groundwater rather than by the distribution of monitoring points alone. After a review of the literature，it was found that the electron acceptor response in microbial degradation is in the order of O₂＞NO₃^-＞Mn⁴⁺，only when the nitrate degradation reaction has been substantially completed does petroleum degradation lead to the release of Mn²⁺.
　　It was found that the concentration of Mn²⁺ is highest when the concentration of nitrate is lowest and that all monitoring points have concentrations below 1 mg/L in the dispersion of Mn²⁺，which is consistent with the theoretical mechanism. Among them，the highest concentration of Mn²⁺ at the M03 point indicates that the degradation of nitrate in the region is largely complete，leading to a large release of Mn²⁺. This finding further confirms that Mn²⁺ migration is influenced by the degradation of petroleum and that the removal of nitrate is a key premise for Mn²⁺ release.
　　In summary，this study verifies the mechanism of microbial degradation and Mn²⁺ release described in the literature by means of data. It shows that in petroleum-contaminated areas the dispersion of Mn²⁺ is influenced not only by the degree of degradation of petroleum but also by the direction of groundwater. This finding helps to explain the migration patterns of pollutants in groundwater system more accurately，and provides scientific basis for groundwater pollution control.
　　This study shows that the GA-BP neural network has obvious advantages in the characterization of pollutant diffusion range，its prediction error is low，the fitting effect is good，and even in the case of limited sampling points and uneven distribution，it can still maintain a high prediction accuracy. Compared with traditional GIS spatial interpolation methods (Kriging method，inverse distance weighting method，spline function，etc.)，the GA-BP neural network is more accurate in pollutant concentration prediction and diffusion range delineation，and the results are more consistent with the migration mechanism and hydrodynamic conditions of pollutants.
　　Furthermore，this study found that the migration of Mn²⁺ is mainly influenced by the combination of the degradation of petroleum pollutants and the flow of groundwater. In areas where the nitrate concentration is near zero and the concentration of petroleum pollutants is moderate，the concentration of Mn²⁺ is highest and migrates along groundwater flows，forming three Mn²⁺ rich areas in the center of the industrial park，the north-west corner and the south-east corner. The revised Mn²⁺ diffusion range is more consistent with known migration laws and further validates the accuracy of the GA-BP neural network predictions.
　　Nevertheless，this study has limitations. The limited number and uneven distribution of sampling points may introduce certain errors in model predictions. In the future，more field monitoring data can be combined to further optimize the parameters of the GA-BP neural network model and improve the accuracy and applicability of prediction.

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References (34)

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