MATEC Web Conf.
Volume 246, 20182018 International Symposium on Water System Operations (ISWSO 2018)
|Number of page(s)||9|
|Section||Main Session: Water System Operations|
|Published online||07 December 2018|
Water demand forecast model of Least Squares Support Vector Machine based on Particle Swarm Optimization
1 Zhejiang Institute of Hydraulics & Estuary, No.50, Fengqi East Road, Hangzhou 310020, China.
2 College of Hydrology and Water Resources, Hohai University, No.1, Xikang Road, Nanjing 210098, China.
a Corresponding author: firstname.lastname@example.org
In order to solve the problem of precision of water demand forecast model, a coupled water demand forecast model of particle swarm optimization (PSO) algorithm and least squares support vector machine (LS-SVM) are proposed in this paper. A PSO-LSSVM model based on parameter optimization was constructed in a coastal area of Binhai, Jiangsu Province, and the total water demand in 2009 and 2010 were simulated and forecasted with the absolute value of the relative errors less than 2.1%. The results showed that the model had good simulation effect and strong generalization performance, and can be widely used to solve the problem of small- sample, nonlinear and high dimensional water demand forecast.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.