Application of a GA-BP Neural Network in Accurately Characterizing the Diffusion Range of Groundwater Pollutants
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摘要:
自2021年最新生态环境损害鉴定评估指南发布实施以来，对地下水中污染物(如铬、铅、铁、锰等污染物)的扩散范围刻画的精度要求越来越高。受研究区场地条件限制，采样点无法完全分布均匀，现有插值方法难以解决采样点分布不均而导致扩散范围刻画不准确的问题。本文通过ArcGIS空间插值图展示某化工园区地下水溶质的空间分布，发现Mn2+离子分布与其形成机制规律相差较大，且尝试使用GIS多种插值方法(如克里金法、反距离权重法、样条函数等插值方法)效果均不理想，其扩散方向与研究区地下水流向及形成机理不符，可能是由于其监测点位分布不均。因此以重金属Mn2+为例，使用GA-BP神经网络与标准BP神经网络对园区各点位Mn2+浓度进行回归预测，建立其浓度与空间分布的神经网络模型，选取拟合程度较好的神经网络模型对监测点位缺失区域进行浓度预测，并结合空间插值圈定化工园区中心Mn2+的扩散范围，同时用Mn2+的产生机制对扩散范围进行验证。结果表明：GA-BP神经网络的Mn2+浓度预测效果最好，使用其补充监测点缺失位置的Mn2+浓度并重新绘制Mn2+浓度分布图，新Mn2+分布图显示化工园区中心Mn2+扩散范围为1.70×106m2，超出化工园区面积为2.13×105m2。与优化前的扩散范围相比，校正后的扩散范围符合Mn2+产生和运移规律。GA-BP神经网络对场地地下水污染物扩散范围的精确圈定有较好的辅助效果，可为环境污染评估提供更加科学有效的方法支持。
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. The BRIEF REPORT is available for this paper at
http://www.ykcs.ac.cn/en/article/doi/10.15898/j.ykcs.202409280204 .-
Key words:
- groundwater /
- chemical industrial park /
- GA-BP neural network /
- influence range
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表 1 地下水样品分析测试方法
Table 1. Analysis methods for groundwater sample
检测项目
Analytical items分析测试方法
Analysis methods方法检出限
Method detection limit硝酸盐 Nitrate 离子色谱法
Ion chromatography0.004mg/L 锰 Manganese 电感耦合等离子体质谱法
Inductively coupled plasma mass spectrometer0.00012mg/L 石油类 Oil 红外分光光度法
Infrared spectrophotometry0.06mg/L 石油烃(C10～C40)
Petroleum hydrocarbons ( C10～C40 )气相色谱法
Gas chromatography0.004mg/L 丙酮 Acetone 气相色谱法
Gas chromatography0.2mg/L 4-硝基苯胺 4-Nitroaniline 气相色谱-质谱法
Gas chromatography-mass spectrometry4.6μg/L 苯酚 Phenol 气相色谱-质谱法
Gas chromatography-mass spectrometry0.1μg/L 甲苯 Toluene 气相色谱-质谱法
Gas chromatography-mass spectrometry0.3μg/L 乙苯 Ethylbenzene 气相色谱-质谱法
Gas chromatography-mass spectrometry1.2μg/L 间二甲苯+对二甲苯 m-Xylene+p-Xylene 气相色谱-质谱法
Gas chromatography-mass spectrometry1.2μg/L 邻二甲苯 o-Xylene 气相色谱-质谱法
Gas chromatography-mass spectrometry1.2μg/L 
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表 2 研究区监测数据
Table 2. Monitoring data of the study area
点位
Point positions锰
Manganese
(mg/L)苯酚
Phenol
(µg/L)丙酮
Acetone
(µg/L)硝基苯
Nitrobenzene
(µg/L)4-硝基苯胺
4-Nitroaniline
(µg/L)石油烃(C10～C40)
Petroleum
hydrocarbons
(C10-C40)
(mg/L)石油类
Oil
(mg/L)甲苯
Toluene
(µg/L)乙苯
Ethylbenzene
(µg/L)间-二甲苯+
对-二甲苯
m-Xylene+
p-Xylene
(µg/L)邻-二甲苯
o-Xylene
(µg/L)M01 0.184 0.3 — — — 0.24 0.45 — — — — M02 0.18 0.2 — — — 2.37 0.29 2.4 — — — M03 0.768 0.1 — — — 5.46 0.98 — 0.4 1.3 0.6 M04 0.031 1.1 — 0.00864 39.7 34.8 1.64 18.2 0.4 1.2 0.4 M05 0.0658 — — 0.00552 6.3 0.62 0.11 2.3 — — — M06 0.0452 — — — — 0.14 0.11 — — — — M07 0.0752 — — — — 2.78 0.17 1 — — — M08 0.182 0.4 — — — 23.5 2.52 — — — — M09 0.0024 — — — — 0.55 0.17 — — — — M10 0.18 0.5 — — — 27.6 2.95 — — — — M11 0.194 — 58 — — 8.95 8.98 — — — — M12 0.0018 — — — — 0.02 0.03 — — — — 注：“—”表示未检出。
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表 3 隐含层节点的确定过程
Table 3. Determination process of hidden layer nodes
隐藏层节点
Hidden layer node训练集的均方误差
Mean square error of training set隐藏层节点
Hidden layer node训练集的均方误差
Mean square error of training set2 0.12601 8 0.67715 3 0.27314 9 0.32133 4 0.11288 10 0.2487 5 0.20273 11 0.06521 6 0.031883 12 2.0155 7 0.069991
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表 4 GA-BP、BP神经网络预测结果与误差
Table 4. Prediction results and errors of GA-BP and BP neural network
样本序号
Sample serial numberMn2+实测值
Mn2+ measured value (mg/L)BP
预测值
(mg/L)GA-BP
预测值
(mg/L)BP误差
(mg/L)GA-BP
误差
(mg/L)1 0.1800 −0.1366 0.1849 −0.3166 0.0049 2 0.1940 −0.1669 0.1876 −0.3609 −0.0064 3 0.0018 −0.1581 0.0057 −0.1599 0.0039
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表 5 BP神经网络误差
Table 5. Error of BP neural network
BP神经网络种类
Error in types of BP neural network误差 mae mse rmse mape 标准的BP神经网络模型
Standard BP neural network model0.279 0.085 0.292 3082.097% 遗传算法优化的BP神经网络模型
Genetic algorithm optimized BP
neural network0.005 2.694×106 0.005 75.066%
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