水利水电技术2018，Vol.49Issue(4)：1-7,7.DOI:10.13928/j.cnki.wrahe.2018.04.001
基于遗传算法的BP神经网络模型在地下水埋深预测中的应用——以蒙城县为例
Application of genetic algorithm-based BP neural network model to prediction of groundwater groundwater buried depth—a case study of Mengcheng County
摘要
Abstract
The groundwater system was a highly complex system.A genetic algorithm-based optimal BP neural network model for shallow groundwater buried depth is established for the non-linear mapping relation between the groundwater level and its impacting factors,with which the groundwater buried depth is simulated and predicted.The results are compared with those from the BP neural network model and stepwise regression model with three evaluating indexes,i.e.RMSE,MAPE and NSE.By taking the antecedentrainfall and antecedent groundwater buried depth of Mengcheng County from 1974 to 1999 and the antecedent groundwater buried depth of Lixin as the input layers,taking the groundwater buried depth of the same month as the output layer and taking groundwater depth of Mengcheng County from 2000 to 2010 as the testing sample,the result shows that RMSE during the training phase and testing phase of the genetic algorithm-based optimal BP neural network model are 0.22 and 0.34 respectively,while MAPE are 7.6％ and 9.21％ and NSE are 0.89 and 0.85 respectively with better generalization performance,and then the over-fitting phenomenon is effectively avoided,while the relevant fitting and predicting accuracies are higher as well.This model can provide an effective method of predicting shallow groundwater buried depth for the study made on groundwater,which has a better application prospect.关键词
GA-BP神经网络/遗传算法/地下水埋深/预测/蒙城县/人工智能算法/地下水资源开发利用与保护/地下水环境保护Key words
GA-BP neural network/genetic algorithm/groundwater depth/prediction/Mengcheng County/artificial intelligence algorithm/exploitation,utilization and protection of grourdwater resources/environmental protection of groundwater分类
地质学引用本文复制引用
陈笑,王发信,戚王月,周婷..基于遗传算法的BP神经网络模型在地下水埋深预测中的应用——以蒙城县为例[J].水利水电技术,2018,49(4):1-7,7.基金项目
国家自然科学基金项目(51509001) （51509001）
安徽省自然科学基金项目(1608085QE112) （1608085QE112）
安徽省高校优秀青年人才支持计划项目(gxyqZD2017019) （gxyqZD2017019）
