[1]张斌,刘俊民.基于BP神经网络的地下水动态预测[J].水土保持研究,2012,19(05):235-237.
　ZHANG Bin,LIU Jun-min.Prediction of Groundwater Dynamics Based on the BP Neural Network[J].Research of Soil and Water Conservation,2012,19(05):235-237.
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基于BP神经网络的地下水动态预测

《水土保持研究》[ISSN:1005-3409/CN:61-1272/P] 卷: 19 期数: 2012年05期 页码: 235-237 栏目: 出版日期: 2012-10-20
Title:
Prediction of Groundwater Dynamics Based on the BP Neural Network
作者:
张斌, 刘俊民
西北农林科技大学, 水利与建筑工程学院, 陕西, 杨凌712100
Author(s):
ZHANG Bin, LIU Jun-min
College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, Shaanxi 712100, China
关键词:
人工神经网络; 地下水位; 动态趋势
Keywords:
artificial neural network; groundwater level; dynamic trend
分类号:
P345
摘要:
人工神经网络是一种高度非线性的并行分布处理系统，采用人工神经网络预测宝鸡市的地下水位动态变化趋势，取1995-2007年研究区内的降水入渗补给量、河道渗漏补给量、人工开采量和闸坝蓄水渗漏量作为输入因子，建立BP模型，用于模拟2008的年地下水位埋深，并与传统的灰色模型进行比较，结果表明：BP神经网络的相对误差介于0.07%～1.98%，相对于灰色模型(0.13%～6.41%)具有较高的预测精度，可为该灌区地下水位的动态预报提供参考。
Abstract:
Artificial neural network is a highly nonlinear parallel distributed processing system. It was used to predict groundwater level in Baoji City. Precipitation infiltration quantity, river leakage recharge, groundwater withdrawal and dam water leakage from 1995 to 2007 were adopted as the input factors to establish BP model to simulate water table depth in 2008. It showed that BP neural network model had high accuracy and the relative error was between 0.07% and 1.98% in comparison with Grey Model(relative error was from 0.13% to 6.41%). Overall, BP neural network model can provide references for groundwater level regime forecast in irrigation zone of Baoji.

参考文献/References:

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备注/Memo

收稿日期:2012-3-24;改回日期:2012-4-12。
基金项目:国家科技支撑计划项目(2006BAD11B05);国家自然科学基金项目(50879071)
作者简介:张斌(1987- )，男，陕西渭南人，硕士研究生，主要从事水资源持续利用与管理研究。E-mail：343741906@163.com
通讯作者:刘俊民(1953- )，男，陕西咸阳人，教授，博士生导师，主要从事水文与水资源研究。E-mail：Jmlslx@yahoo.com.cn

更新日期/Last Update: 1900-01-01