Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China

Abstract

Research Article| June 18 2014 Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China Wenxi Lu; Wenxi Lu 1College of Environment and Resources, Jilin University, Changchun 130021, China and Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China E-mail: luwenxi@jlu.edu.cn Search for other works by this author on: This Site PubMed Google Scholar Haibo Chu; Haibo Chu 1College of Environment and Resources, Jilin University, Changchun 130021, China and Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China Search for other works by this author on: This Site PubMed Google Scholar Zheng Zhang Zheng Zhang 1College of Environment and Resources, Jilin University, Changchun 130021, China and Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2015) 64 (1): 95–104. https://doi.org/10.2166/aqua.2014.002 Article history Received: December 25 2013 Accepted: May 23 2014 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation Wenxi Lu, Haibo Chu, Zheng Zhang; Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China. Journal of Water Supply: Research and Technology-Aqua 1 February 2015; 64 (1): 95–104. doi: https://doi.org/10.2166/aqua.2014.002 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Rainfall is a key part of the hydrological cycle, and correct forecasting of rainfall is vital in the planning and management of water resources. Generalized regression neural network (GRNN) and support vector regression (SVR) were both applied to forecast monthly rainfall, and the conventional autoregressive model was built for comparison. Furthermore, Akaike Information Criteria were used to identify the proper inputs for the rainfall forecasting model. The data sets of monthly rainfall for a 53-year period from 1957 to 2010 in western Jilin Province, China, were used. The results indicated that the proper inputs would help in effectively improving the prediction accuracy. Furthermore, the results showed that both the SVR and the GRNN model performed better than the autoregressive model in forecasting monthly rainfall. SVR models outperformed all other models during the testing period in terms of the mean absolute error, root-mean-square error, coefficient of efficiency and R2. Therefore, SVR models were applied to forecast monthly rainfall for six cities including Baicheng, Qianguo, Fuyu, Qian'an, Changling and Tongyu. generalized regression neural network, monthly rainfall forecasting, support vector regression, western Jilin Province © IWA Publishing 2015 You do not currently have access to this content.