应用GRNN神经网络模型计算西北干旱区内陆河流域出山径流  

Application of the generalized regression neural network to simulating runoff from the mountainous watersheds of inland river basins in the arid area of northwest China

作　　者：陈仁升[1] 康尔泗[1] 张济世[1] 

机构地区：[1]中国科学院寒区旱区环境与工程研究所,甘肃兰州730000

出　　处：《水科学进展》2002年第1期87-92,共6页Advances in Water Science

基　　金：中国科学院知识创新工程重大项目 (KZCX1 1 0 0 3 0 1 ) ;国家自然科学基金重点项目( 4 9731 0 30 )~~

摘　　要：根据全球变化对 2 0 30年降水量和气温的预测结果 ,设置不同的气候变化情景 ,应用GRNN模型对黑河出山径流进行了预测。结果表明 ,到 2 0 30年 ,黑河出山径流将有小幅度的增加。随着气温的不断上升 ,出山年径流量最终将减少。According to the results of globle changing in the mountainous precipitation and air temperature of Northwest China and supposing possible several conditions of the precipitation and air temperature, this paper uses the generalized regression neural network model to predict the runoff of the year 2030. The predicted results show that the runoff may arise to the year 2030, but the arising degree is not large, and ultimately the runoff will decrease with arising of the air temperature.

关 键 词：内陆河流域 出山径流 气候变化 GRNN神经网络 西北干旱区 

分 类 号：TV121[水利工程—水文学及水资源]