首页|人工神经网络在我国西南地区岩溶地下水污染评价中的应用

人工神经网络在我国西南地区岩溶地下水污染评价中的应用

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岩溶地下水是人类生活的重要用水来源,预测岩溶地下水污染水平对可持续开发利用地下水资源和保护岩溶地区生态环境具有重要意义.为获取更可靠的岩溶地下水污染水平预测结果,利用我国西南部典型岩溶区 105 组地下水样品,在内梅罗综合污染指数评价量化结果基础上,分别采用 BP(Back Propagation)神经网络、SVM(Support Vector Machine)和 T-S(Takagi-Sugeno)模糊神经网络进行地下水污染等级预测,比较不同人工神经网络污染评价预测结果的可靠性.利用 90 组岩溶地下水样品进行人工神经网络训练,剩余 15 组岩溶地下水样品进行污染等级预测比拟.结果表明,BP神经网络模型预测结果与内梅罗综合污染指数评价结果相似性为 80%,SVM相似性为 73.3%,T-S模糊神经网络相似性为 66.7%.BP神经网络预测的岩溶地下水污染等级结果与内梅罗综合污染指数评价量化结果最为相近,且优于SVM和T-S模糊神经网络预测结果.基于此,在我国西南部岩溶地下水污染评价研究中,建议优先选用BP人工神经网络进行岩溶地下水污染水平预测.
Application of artificial neural network to karst groundwater pollution assessment in Southwest China
Karst groundwater is an important source of water for human life.Predicting the pollution level of karst groundwater is of great significance for the sustainable development and utilization of groundwater resources and the protection of the ecological environment in karst areas.In order to obtain more representative evaluation results of karst groundwater pollution,a total of 105 groundwater samples were collected from typical karst areas,southwestern China.On the basis of the quantitative results of Nemero Comprehensive Pollution Index,the BP(Back Propagation)neu-ral network,SVM(Support Vector Machine)and T-S(Takagi-Sugeno)fuzzy neural network were used to predict groundwater pollution level.Further,the reliabilities of three artificial neural net-work conducted in pollution evaluation and prediction were compared.In these processes,90 karst groundwater samples were applied to train artificial neural network,and remaining 15 karst groundwater samples were employed for predicting the pollution level.The results exhibited that the similarity between the prediction results of BP neural network model and the evaluation re-sults of Nemero comprehensive pollution index is 80%,the similarity of SVM is 73.3%,and the similarity of T-S fuzzy neural network is 66.7%.The prediction result of karst groundwater pol-lution level from BP neural network is better than that from the SVM,and also better than that from the T-S.Moreover,the obtained prediction result from BP neural network is similar to the result from the Nemero Comprehensive Pollution Index.Based on this,in the study of karst groundwater pollution evaluation in southwest China,it is suggested that BP artificial neural net-work should be preferred to predict the pollution level of karst groundwater.

karst undergroundpollution assessmentNemero Comprehensive Pollution IndexArtificial Neural Network

刘伟、刘昊洋、朱天龙、谢浩、邹胜章、杨国丽、马立山、李军

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河北建筑工程学院,河北 张家口 075000

中国地质科学院岩溶地质研究所,自然资源部/广西岩溶动力学重点实验室,广西 桂林 541004

联合国教科文组织国际岩溶研究中心,广西 桂林 541004

岩溶地下水 污染评价 内梅罗污染指数 人工神经网络

2024

河北建筑工程学院学报
河北建筑工程学院

河北建筑工程学院学报

影响因子:0.502
ISSN:1008-4185
年,卷(期):2024.42(3)