西北水电Issue(3)：6-9,99,5.DOI:10.3969/j.issn.1006-2610.2015.03.002
基于人工神经网络模型的地下水水位动态变化模拟
Dynamic Variation Simulation of Ground Water Table Based on Artificial Neural Network Model
摘要
Abstract
Predication of the ground water table plays an important role in planning management of catchement surface and ground water resources.In this study, the artificial neural network model is applied in predication of the ground water table around the Xinier reser-voir.By application of data from 6 monitoring wells in the study area and of the artificial neural network model, the ground water table af-ter one week is predicated by simulation.The factors input the model include evaporation, reservoir level, escape canal level, water pumped volume and ground water table of the monitoring wells in last week.Therefore, the model is with 15 input points and 6 output points.Three different neural network methods of GDX, LM and BR methods are applied for the predication of the ground water table. The study shows that all three methods perform well in the predication.Generally, BR performance is better than these of GDX and LM. The artificial neural network model trained by BR method is applied for the predication of the ground water table in future 2nd, 3rd and 4th weeks in the study area.The simulation results are still better although the accuracy of the predication of the ground water table slight-ly decreases with time increment.关键词
人工神经网络/地下水位预测/GDX算法/LM算法/BR算法Key words
artificial neural network model/predication of ground water table/GDX method/LM method/BR method分类
天文与地球科学引用本文复制引用
魏光辉..基于人工神经网络模型的地下水水位动态变化模拟[J].西北水电,2015,(3):6-9,99,5.基金项目
新疆水文学及水资源重点学科资助（ XJSWSZYZDXK2010－12－02）． （）
