Groundwater Level Dynamic Prediction Based on Chaos Optimization and Support Vector Machine

Abstract

Groundwater level has random characters because of influences factors of natural and anthropogenic. Study random prediction model of groundwater level on the basis of groundwater physical process analysis is important to groundwater appraisal. The theory of supporting vector machine based on small-sample machine learning theory is introduced into dynamic prediction of groundwater level. A least square support vector machine groundwater level dynamic forecasting model based on chaos optimization peak value identification was proposed and applied in Hetao irrigation district in Inner Mongolia. The results show that the fitted values, the tested values and the predicted values of this model have little different from their real values. And they indicate that the model is feasible and effective. So the model proposed in this paper can provide a new tool for groundwater level dynamic forecasting.