{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T17:00:02Z","timestamp":1765040402171},"reference-count":35,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2013,6]]},"abstract":"<jats:p> Accurate forecasting of rainfall has been one of the most important issues in hydrological research such as river training works and design of flood warning systems. Support vector regression (SVR) is a popular regression method in rainfall forecasting. Type of kernel function and kernel parameter setting in the SVR traing procedure, along with the input feature subset selection, significantly influence regression accuracy. In this paper, an effective hybrid optimization strategy by combining the strengths of genetic algorithm (GA) and simulated annealing (SA), is employed to simultaneously optimize the input feature subset selection, the type of kernel function and the kernel parameter setting of SVR, namely GASA\u2013SVR. The developed GASA\u2013SVR model is being applied for monthly rainfall forecasting in Guilin of Guangxi. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima and avoid sinking into the local optimal solution early. Compared with SVR, pure GA\u2013SVR and HGA\u2013SVR, results show that the hybrid GASA\u2013SVR model can correctly select the discriminating input features subset, successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall forecasting, can also significantly improve the rainfall forecasting accuracy. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed GASA\u2013SVR model provides a promising alternative to monthly rainfall prediction. <\/jats:p>","DOI":"10.1142\/s1469026813500120","type":"journal-article","created":{"date-parts":[[2013,6,26]],"date-time":"2013-06-26T06:46:46Z","timestamp":1372229206000},"page":"1350012","source":"Crossref","is-referenced-by-count":8,"title":["HYBRID OF GENETIC ALGORITHM AND SIMULATED ANNEALING FOR SUPPORT VECTOR REGRESSION OPTIMIZATION IN RAINFALL FORECASTING"],"prefix":"10.1142","volume":"12","author":[{"given":"CHANGMING","family":"ZHU","sequence":"first","affiliation":[{"name":"School of Physics and Electronic Engineering, Guangzhou University, Guangzhou 510006, China"}]},{"given":"JIANSHENG","family":"WU","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China"},{"name":"Department of Mathematical and Computer Sciences, Liuzhou Teachers College, Liuzhou, Guangxi 545004, China"}]}],"member":"219","published-online":{"date-parts":[[2013,6,27]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1002\/hyp.5638"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1142\/S1469026810002793"},{"key":"rf3","first-page":"46","volume":"246","author":"Gwangseob K.","year":"2001","journal-title":"J. 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