Normal water level scheme optimization based on GIS and multi-population genetic neural network model(基于GIS与多子群遗传神经网络模型的水位方案优选)

GAOMei-na(郜美娜); YANJun(严俊); LIURen-yi(刘仁义); ZHANGFeng(张丰)

doi:10.3785/j.issn.1008-9497.2013.06.019

Zhejiang Daxue xuebao. Lixue ban (Nov 2013)

Normal water level scheme optimization based on GIS and multi-population genetic neural network model(基于GIS与多子群遗传神经网络模型的水位方案优选)

GAOMei-na(郜美娜),
YANJun(严俊),
LIURen-yi(刘仁义),
ZHANGFeng(张丰)

Affiliations

GAOMei-na(郜美娜): 1. Zhejiang Provincial Key Lab of GIS, Zhejiang University, Hangzhou 310028, China( 1.浙江大学浙江省资源与环境信息系统重点实验室，浙江　杭州　310028)
YANJun(严俊): 2. Zhejiang Province Ocean Monitoring Forecasting Center, Hangzhou 310007, China( 2.浙江省海洋监测预报中心，浙江　杭州　310007)
LIURen-yi(刘仁义): 3. Department of Geographic Information Science, Zhejiang University, Hangzhou 310027, China( 3.浙江大学地理信息科学研究所，浙江　杭州　310027)
ZHANGFeng(张丰): 1. Zhejiang Provincial Key Lab of GIS, Zhejiang University, Hangzhou 310028, China( 1.浙江大学浙江省资源与环境信息系统重点实验室，浙江　杭州　310028)

DOI: https://doi.org/10.3785/j.issn.1008-9497.2013.06.019
Journal volume & issue: Vol. 40, no. 6
pp. 710 – 715

Abstract

Read online

水利水电工程决策具有多目标、多层次、多影响因素的特点.针对现有正常蓄水位方案形成过程的不足，建立了基于GIS辅助分析、从蓄水位限值确定到获取方案指标、从方案形成到各方案比选的一整套流程方法.为解决正常蓄水位方案优选的非线性问题，避免综合优选模型中依靠主观经验赋权的人为因素影响，提出将多子群遗传神经网络模型应用于正常蓄水位方案优选.该方法有效减少了人为主观因素的影响，提高了指标权重确定的客观程度，为正常蓄水位方案的优选提供了新思路和解决办法.

Published in Zhejiang Daxue xuebao. Lixue ban

ISSN: 1008-9497 (Print)
Publisher: Zhejiang University Press
Country of publisher: China
LCC subjects: Science: Mathematics: Instruments and machines: Electronic computers. Computer science; Science: Physics
Website: https://www.zjujournals.com/sci/EN/1008-9497/home.shtml

About the journal

Abstract

Keywords