GROUNDWATER VULNERABILITY EVALUATION AND RISK CONTROL IN A CERTAIN AREA IN NORTHERN GUANGDONG PROVINCE BASED ON BP NEURAL NETWORK

ZHANG Tao; WANG Xiahui; BI Erping; HUANG Guoxin; YANG Ruijie

doi:10.13205/j.hjgc.202312034

Volume 41 Issue 12

Dec. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(12): 270-277

ZHANG Tao, WANG Xiahui, BI Erping, HUANG Guoxin, YANG Ruijie. GROUNDWATER VULNERABILITY EVALUATION AND RISK CONTROL IN A CERTAIN AREA IN NORTHERN GUANGDONG PROVINCE BASED ON BP NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(12): 270-277. doi: 10.13205/j.hjgc.202312034

Citation:

ZHANG Tao, WANG Xiahui, BI Erping, HUANG Guoxin, YANG Ruijie. GROUNDWATER VULNERABILITY EVALUATION AND RISK CONTROL IN A CERTAIN AREA IN NORTHERN GUANGDONG PROVINCE BASED ON BP NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(12): 270-277. doi: 10.13205/j.hjgc.202312034

Citation:

ZHANG Tao, WANG Xiahui, BI Erping, HUANG Guoxin, YANG Ruijie. GROUNDWATER VULNERABILITY EVALUATION AND RISK CONTROL IN A CERTAIN AREA IN NORTHERN GUANGDONG PROVINCE BASED ON BP NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(12): 270-277. doi: 10.13205/j.hjgc.202312034

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GROUNDWATER VULNERABILITY EVALUATION AND RISK CONTROL IN A CERTAIN AREA IN NORTHERN GUANGDONG PROVINCE BASED ON BP NEURAL NETWORK

doi: 10.13205/j.hjgc.202312034

1. Shandong Huankeyuan Environmental Testing Co., Ltd., Jinan 250013, China;
2. Chinese Academy of Environmental Planning, Beijing 100041, China;
3. School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, China;
4. Shaoguan Environmental Pollution Control Center, Shaoguan 512026, China

Received Date: 2022-03-01
Available Online: 2024-03-08

Abstract

Abstract

Aiming at the high subjectivity of the traditional DRASTIC model in the process of the parameter weight determination, taking shallow groundwater in a certain area in northern Guangdong Province as a study target, the BP neural network was optimized and the DRASTICL model was constructed by using the collected shallow groundwater-related data and adding land use type parameter. On this basis, groundwater NO₃^- concentration was used to verify the models, and further the BP neural network and the constructed DRASTICL model were coupled to establish a BP-DRASTICL model. Finally, risk control strategies were suggested according to the spatial distribution characteristics of groundwater vulnerability. The results showed that when the training function was trainlm, the learning rate was 0.1, and the number of hidden layer neurons was 6, BP neural network performed best, and accordingly the optimal BP-DRASTICL parameter weights were 0.1420 (groundwater depth, D), 0.1151 (recharge, R), 0.0791 (aquifer media, A), 0.1833 (soil medium, S), 0.0908 (topography, T), 0.1574 (influence of vadose zone media, I), 0.0891 (hydraulic conductivity, C) and 0.1433 (land use type, L). D, S, T and L had the greatest influence on the evaluation results. Compared with the DRASTIC models and the DRASTICL model, the BP-DRASTICL model had the highest Pearson (0.615) and Spearman (0.656) correlation coefficients, indicating a high linear correlation between the actual nitrate concentration and the vulnerability index. The groundwater vulnerability was generally in the extremely low, and low level, across the study area, and the areas with the high and extremely high vulnerability level were mainly distributed in the middle of the study area. According to the spatial distribution characteristics of the vulnerability, differentiated strategies were proposed for groundwater pollution risk control. Using the BP neural network to determine the parameter weights of the DRASTICL model is more accurate than using the traditional expert scoring method, because it reduces the influence of human subjectivity.
- DRASTIC model,
- groundwater vulnerability,
- machine learning,
- parameters weighting

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References(28)

References

[1]	李怀恩, 贾斌凯, 成波, 等. 海绵城市雨水径流集中入渗对土壤和地下水影响研究进展[J]. 水科学进展, 2019, 30(4):589-600.
[2]	刘乃静, 赵银鑫, 吴文忠, 等. 基于AHP的DRASTIC模型在银川市潜水防污性能评价中的应用[J]. 水土保持通报, 2021, 41(1):212-218.
[3]	王昕翌, 王敏, 冯建国, 等. 地下水水源地保护区划分方法研究:以大汶口为例[J]. 人民长江, 2021, 52(4):61-67.
[4]	AKINLALU A A, MOGAJI K A, ADEBODUM T S. Assessment of aquifer vulnerability using a developed "GODL" method (modifed GOD model) in a schist belt environ, Southwestern Nigeria[J]. Environmental Monitoring and Assessment, 2021, 193(4):199-226.
[5]	ZHANG L Z, ZHANG Y B, ZHOU X Y, et al. Groundwater vulnerability assessment system based on GIS[J]. Advanced Materials Research, 2011, 301(303):724-730.
[6]	吕文凯, 周金星, 万龙, 等. 滇东岩溶断陷盆地水资源脆弱性评价[J]. 地球学报, 2021, 42(3):341-351.
[7]	张钧帅, 汪丙国, 刘天奇. 江汉平原浅层地下水防污性能模糊综合评价与验证[J]. 地质科技通报, 2020, 39(6):154-164.
[8]	MFONKA Z, NGOUPAYOU J R N, NDJIGUI P D, et al. A GIS-based DRASTIC and GOD models for assessing alterites aquifer of three experimental watersheds in Foumban (Western-Cameroon)[J]. Groundwater for Sustainable Development, 2018, 7:250-264.
[9]	于林弘, 陶志斌, 扈胜涛, 等. DRASTIC模型在地下水脆弱性评价中的应用[J]. 人民黄河, 2020, 42(增刊1):45-46, 50.
[10]	杨宁, 陶志斌, 高松, 等. 基于AHP的DRASTIC模型对莱州地区地下水脆弱性研究[J]. 地质学报, 2019, 93(增刊1):133-137.
[11]	朱飞, 熊丽君, 吴建强, 等. 基于改进DRASTIC模型的平原河网地区地下水脆弱性评价[J]. 环境科学与技术, 2020, 43(2):187-193.
[12]	王建红, 余启明, 李平平. 基于GIS技术与DRASTIC模型的民勤盆地地下水脆弱性评价[J]. 兰州大学学报(自然科学版), 2015, 51(6):882-887.
[13]	ASLAM B, ISMAIL S, ALI I. A GIS-based DRASTIC model for assessing aquifer susceptibility of Safdarabad Tehsil, Sheikhupura District, Punjab Province, Pakistan[J]. Modeling Earth Systems and Environment, 2020, 6:995-1005.
[14]	李亮, 王敏, 邢怀学, 等. 基于AHP-DRASTIC评价模型的无锡市潜水污染风险评价[J]. 合肥工业大学学报(自然科学版), 2019, 42(3):310-314.
[15]	周书葵, 江海浩, 陈朝猛, 等. 改进AHP-DRASTIC模型用于地下水U(Ⅵ)污染风险评价及回归分析[J]. 环境工程, 2016, 34(1):130-134.
[16]	YANG J, TANG Z H, JIAO T, et al. Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain, China[J]. Environmental Earth Science, 2017, 76(12):426-442.
[17]	贺新春, 邵东国, 陈南祥, 等. 几种评价地下水环境脆弱性方法之比较[J]. 长江科学院院报, 2005, 22(3):17-20 , 24.
[18]	徐超, 周嘉月, 何旭佳, 等. 基于改进DRASTIC模型的陕西省地下水脆弱性评价[J]. 中国农村水利水电, 2020(3):44-51.
[19]	任加国, 龚克, 马福俊, 等. 基于BP神经网络的污染场地土壤重金属和PAHs含量预测[J]. 环境科学研究, 2021, 34(9):2237-2247.
[20]	KUANG Y T, SINGH R, SINGH S, et al. A novel macroeconomic forecasting model based on revised multimedia assisted BP neural network model and ant Colony algorithm[J]. Multimedia Tools and Applications, 2017, 76(18):18749-18770.
[21]	张天云, 陈奎, 魏伟, 等. BP神经网络法确定工程材料评价指标的权重[J]. 材料导报, 2012, 26(1):159-163.
[22]	赵军, 张祯宇, 谢哲宇, 等. 基于BP人工神经网络的闽江口水厂水质模拟[J]. 环境科学与技术, 2020, 43(增刊1):198-203.
[23]	黄元生, 张利君. 基于遗传算法的BP-LSSVM组合变权模型权重优化的短期电价预测研究[J]. 煤炭工程, 2019, 51(5):172-176.
[24]	孙会君, 王新华. 应用人工神经网络确定评价指标的权重[J]. 山东科技大学学报(自然科学版), 2001, 20(3):84-86.
[25]	钟佐燊. 地下水防污性能评价方法探讨[J]. 地学前缘, 2005, 12(增刊1):3-11.
[26]	IIAMURUGAN O, JOTHIBASU A, ANBAZHAGAN S. Geospatial technology and modified DRASTIC model to assess the groundwater pollution vulnerability along a stretch of Cauvery River, South India[J]. Environmental Earth Science, 2022, 81:85.
[27]	YU H, WU Q, ZENG Y F, et al. Integrated variable weight model and improved DRASTIC model for groundwater vulnerability assessment in a shallow porous aquifer[J]. Journal of Hydrology, 2022, 608:127538.
[28]	陈红兵, 孙俊辉, 王聪聪, 等. 应用遗传算法优化BP神经网络预测太阳能PV/T系统热电产出[J]. 热科学与技术, 2021, 20(5):480-487.