Multi-objective particle swarm optimization on calibration of hydrological model

被引：0

作者：

Li, Chuanzhe ^{[1
]}

Liu, Jia ^{[2
]}

Lu, Fan ^{[1
]}

Yan, Denghua ^{[1
]}

Yu, Fuliang ^{[1
]}

机构：

[1] China Inst Water Resources & Hydropower Res, Dept Water Resources, Beijing, Peoples R China

[2] Univ Bristol, Dept Civil Engn, Water & Environm Management Res Ctr, Bristol, Avon, England

来源：

2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL VIII | 2010年

关键词：

particle swarm optimization; hydrological model; multi-objective; parameters calibration;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the aim of studying the impact of objective functions on the hydrological model parameters, Nash-Sutcliffe efficiency and absolute percentage water balance error are used as performance measures. 331 relatively unimpaired catchments all over Australia with daily precipitation, potential evapotranspiration, and streamflow data are used to drive SIMHYD model using Particle Swarm Optimization (PSO) method to get more general conclusions. The results show that hydrological model calibration using multi-objective can consider of different aspects of hydrograph and have better performance than single-objective calibration. Our results may have useful and interesting implications for hydrological model users.

引用

页码：188 / 191

页数：4

共 5 条

[1] Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River
Chau, K. W.
[J]. JOURNAL OF HYDROLOGY, 2006, 329 (3-4) : 363 - 367
[2] Chiew F. H. S., 2002, Mathematical models of small watershed hydrology and applications, P335
[3] Kennedy J., 1995, PROC 6 INT S MICROMA, P39, DOI DOI 10.1109/MHS.1995.494215
[4] Nash J., 1970, J. Hydrol, V10, P280, DOI [DOI 10.1016/0022-4(70)90255-6, 10.1016/0022-1694(70)90255-6, DOI 10.1016/0022-1694(70)90255-6]
[5] Peel MC, 2000, REPORT NATL LAND WAT

← 1 →