{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:08:54Z","timestamp":1772064534607,"version":"3.50.1"},"reference-count":0,"publisher":"University of Zielona G\u00f3ra, Poland","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,12,28]]},"abstract":"<jats:p>In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the <jats:italic>k<\/jats:italic>-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models.<\/jats:p>","DOI":"10.2478\/v10006-012-0062-1","type":"journal-article","created":{"date-parts":[[2013,2,13]],"date-time":"2013-02-13T15:21:01Z","timestamp":1360768861000},"page":"841-854","source":"Crossref","is-referenced-by-count":85,"title":["A rainfall forecasting method using machine learning models and its application to the Fukuoka city case"],"prefix":"10.61822","volume":"22","author":[{"given":"S. Monira","family":"Sumi","sequence":"first","affiliation":[]},{"given":"M. Faisal","family":"Zaman","sequence":"additional","affiliation":[]},{"given":"Hideo","family":"Hirose","sequence":"additional","affiliation":[]}],"member":"37438","container-title":["International Journal of Applied Mathematics and Computer Science"],"original-title":[],"link":[{"URL":"http:\/\/content.sciendo.com\/view\/journals\/amcs\/22\/4\/article-p841.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/view\/j\/amcs.2012.22.issue-4\/v10006-012-0062-1\/v10006-012-0062-1.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T10:28:06Z","timestamp":1709202486000},"score":1,"resource":{"primary":{"URL":"https:\/\/content.sciendo.com\/doi\/10.2478\/v10006-012-0062-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,12,28]]},"references-count":0,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.2478\/v10006-012-0062-1","relation":{},"ISSN":["2083-8492","1641-876X"],"issn-type":[{"value":"2083-8492","type":"electronic"},{"value":"1641-876X","type":"print"}],"subject":[],"published":{"date-parts":[[2012,12,28]]}}}