%0 Journal Article
%T Dynamic Prediction of Groundwater Level based on Chaos Optimization and Support Vector Machine<br>基于混沌优化的支持向量机地下水位动态预测
%A ZHANG Wen-ge
%A HUANG Qiang
%A TONG Chun-sheng
%A <br>张文鸽
%A 黄强
%A 佟春生
%J 资源科学
%D 2007
%I 
%X Groundwater level has random characters because of influences of natural and anthropogenic factors.So study on random dynamic prediction model of groundwater level on the basis of groundwater physical process analysis is important to groundwater appraisal.The theory of supporting vector machine in small-sample machine learning theory was introduced into dynamic prediction of groundwater level.Considering groundwater level dynamic series length and peak mutation characters,the least squares support vector machine arithmetic based on peak value identification was proposed.Aiming at parameter optimization,training and speed test of supporting vector machine arithmetic,a least square supporting vector machine groundwater level dynamic forecasting model based on chaos optimization peak value identification was built up.At last,based on precipitation,average temperature,transpiration rate,amount of water diversion,ground water mining quantity,ground water excretion quantity and groundwater level burying depth data of summer irrigation periods(April to June),autumn irrigation periods(July to September,October to November)from 1990 to 2004 in Yichang irrigation sub-district of Hetao irrigation district in Inner Mongolia,dynamic prediction model of groundwater level was built up.The results show that the fitted values,the tested values and the predicted values of this model have little difference from their real values.The absolute value of the fitting mean relative error is 2.08 percent;The absolute value of the testing mean relative error is 3.48 percent;The absolute value of the predicting mean relative error is 6.86 percent.At the same time,the model has high training and testing speed.But the absolute value of the predicting mean relative error of the least squares support vector machine model is 20.68 percent.So the model proposed in this paper can provide a new tool for groundwater level dynamic forecasting.
%K Groundwater level
%K Support vector machine
%K Chaos
%K Prediction
%K Optimization<br>地下水位
%K 支持向量机
%K 混沌
%K 预测
%K 优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=B5EDD921F3D863E289B22F36E70174A7007B5F5E43D63598017D41BB67247657&cid=B47B31F6349F979B&jid=9DEEAF23637E6E9539AD99BE6ABAB2B3&aid=2E032297D8318771&yid=A732AF04DDA03BB3&vid=771469D9D58C34FF&iid=94C357A881DFC066&sid=03F1579EF92A5A32&eid=91C9056D8E8856E0&journal_id=1007-7588&journal_name=资源科学&referenced_num=0&reference_num=15