摘要
Traditional methods for water table prediction have such defects as extensive calculation and reliance on the presupposition of a homogeneous and regular aquifer.Based on the fundamentals of the general regression neural network(GRNN),this article sets up a GRNN model for water level prediction.Case study indicates that this model,even with limited information,has satisfactory prediction accuracy,which,coupled with a simple model structure and relatively high calculation efficiency,mean a vast application prospect for the model.
Traditional methods for water table prediction have such defects as extensive calculation and reliance on the presupposition of a homogeneous and regular aquifer. Based on the fundamentals of the general regression neural network( GRNN), this article sets up a GRNN model for water level prediction. Case study indicates that this model,even with limited information, has satisfactory prediction accuracy, which, coupled with a simple model structure and relatively high calculation efficiency, mean a vast application prospect for the model.
出处
《科技视界》
2017年第35期56-57,共2页
Science & Technology Vision
