摘要
地下水埋深预测对于区域水资源管理利用、生态环境保护和经济社会发展等具有重要的价值与作用。地下水埋深受多种因素影响,其动态变化具有非平稳性、随机性和滞后性等特征。为了准确预测浅层地下水埋深,选用多元线性回归、灰色GM(1,1)、基于马尔科夫链优化的灰色GM(1,1)、BP神经网络和基于遗传算法优化的BP神经网络五种预测模型,以黑龙江省肇州县为应用实例,将1980-2009年数据作为训练样本,2010-2019年数据作为检验样本,以降水量、蒸发量、地下水开采量和前期水位作为输入层输入,以地下水埋深作为输出层输出,选择绝对误差、相对误差、平均绝对误差、平均绝对百分比误差、均方误差和均方根误差作为评价指标,进行地下水埋深模拟预测和对比分析。结果表明:基于遗传算法优化的BP神经网络模型的平均绝对误差0.13 m,平均绝对百分比误差1.58%,均方误差0.02,均方根误差0.15,预测精度较高、拟合效果较好,相较于其他4种模型可以更好的模拟地下水埋深动态变化,为肇州县地下水合理开发和利用提供参考;遗传算法优化提升了BP神经网络的训练效率和稳定性,马尔科夫链理论弥补了灰色GM(1,1)所缺少的波动性,组合预测模型两两结合,优势互补,显著提升预测性能相较于单一模型预测结果更加准确,可以为地下水埋深预测模型的建立提供新的思路。
Groundwater depth prediction plays an important role in regional water resources management and utilization ecological environment protection and economic and social development. Groundwater is affected by many factors,and its dynamic change has the characteristics of non-stationary,random and hysteresis. In order to accurately predict the depth of shallow groundwater,five prediction models,including multiple linear regression,gray GM(1,1),gray GM(1,1)based on Markov chain optimization,BP neural network and BP neural network based on genetic algorithm optimization,are selected to take Zhaozhou County,Heilongjiang Province as an example,and the data from 1980 to 2009 are taken as training samples. Data from 2010 to 2019 are used as test samples,precipitation,evaporation,groundwater exploitation and early stage water level are used as input layers,and groundwater depth is used as the output layer. Absolute error,relative error,mean absolute error,mean absolute percentage error,mean square error and root mean square error are selected as evaluation indexes. The results show that:The average absolute error of BP neural network model optimized by genetic algorithm is 0.13m,the average absolute percentage error is 1.58%,the mean square error is 0.02,and the root mean square error is 0.15. The prediction accuracy is high and the fitting effect is good. Compared with the other four models,it can better simulate the dynamic change of groundwater depth. It provides reference for rational development and utilization of groundwater in Zhaozhou County. The optimization of genetic algorithm improves the training efficiency and stability of BP neural network,and the Markov chain theory makes up for the lack of fluctuation of gray GM(1,1).The combined prediction model combines the two models,complementing each other’s advantages and significantly improving the prediction performance. Compared with the single model,the prediction result is more accurate,which can provide a new idea for the establishment of groundwater depth prediction model.
作者
张嗣路
李治军
于博文
王涛
ZHANG Si-lu;LI Zhi-jun;YU Bo-wen;WANG Tao(School of Hydraulic&Electric-power,Heilongjiang University,Harbin 150080,Heilongjiang Province,China;Institute of Groundwater in Cold Region,Heilongjiang University,Harbin 150080,Heilongjiang Province,China)
出处
《中国农村水利水电》
北大核心
2022年第10期119-124,共6页
China Rural Water and Hydropower
基金
国家科技支撑计划项目(2014BAD12B01-03)。
