{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:36:45Z","timestamp":1777703805251,"version":"3.51.4"},"reference-count":46,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2018,7,9]],"date-time":"2018-07-09T00:00:00Z","timestamp":1531094400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,7,27]]},"abstract":"<jats:p>Rainfall prediction is one of the complex nonlinear dynamic phenomena. This is due to uncertainties associated with the climatic parameters used for rainfall prediction. Fuzzy system has the capability to deal with the uncertainties and is efficient when the conventional linear statistical models are not able to perform well due to the nonlinear nature of the climatic parameters. In the present study, a data driven Fuzzy Inference System for high-dimensional data is developed to predict rainfall of the Indian subcontinent. Indian monsoon is an important climatic phenomenon due to its direct impact on socio-economic growth. The parameters Sea Surface Temperature, Sea Level Pressure, El Ni\u00f1o-Southern Oscillation, Indian Ocean Dipole Mode and the Equatorial Indian Ocean Oscillation have been used for analyses and prediction. The variability of Indian rainfall is considered for the period of 25 years from 1990\u20132014 and the possibility of prediction is explored using Fuzzy Inference System. In fuzzy inference system the membership functions are the building blocks and computing its range is a crucial task. We have used triangular membership function and in order to define the range of membership function, this study proposes two methods, divisive method for input parameters and clustering based method for output parameter. The experimental results obtained using the proposed fuzzy inference system is compared with Multiple Linear Regression and Multiple Adaptive Regression Splines. The proposed Fuzzy based predictive model shows better results in terms of the accuracy with 84% and correlation 0.78 between actual and predicted rainfall.<\/jats:p>","DOI":"10.3233\/jifs-171325","type":"journal-article","created":{"date-parts":[[2018,7,10]],"date-time":"2018-07-10T14:38:07Z","timestamp":1531233487000},"page":"807-821","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["A fuzzy inference model for rainfall prediction"],"prefix":"10.1177","volume":"35","author":[{"given":"Rika","family":"Sharma","sequence":"first","affiliation":[{"name":"Department of Computer Applications, National Institute of Technology Raipur, Raipur, CG, India"}]},{"given":"Kesari","family":"Verma","sequence":"additional","affiliation":[{"name":"Department of Computer Applications, National Institute of Technology Raipur, Raipur, CG, India"}]}],"member":"179","published-online":{"date-parts":[[2018,7,9]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"AbrahamA. PhillipN. and JosephK. Will we have a wet summer? Soft computing models for long-term rainfall forecasting In 15th European Simulation Multiconferece Society for Computer Simulation International (2001) pp. 1044\u20131048."},{"key":"e_1_3_1_3_2","unstructured":"AbrahamA. SteinbergD. and PhilipN.S. Rainfall forecasting using soft computing models and multivariate adaptive regression splines IEEE SMC Transactions Special Issue on Fusion of Soft Computing and Hard Computing in Industrial Applications (2001) 1: pp. 1\u20136."},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","unstructured":"AshokK. NakamuraH. and YamagataT. Impacts of ENSO and Indian Ocean dipole events on Southern Hemisphere storm-track activity during austral winter Journal of Climate (2013) 3147\u20133163.","DOI":"10.1175\/JCLI4155.1"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosres.2011.02.015"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2011.2147794"},{"issue":"5","key":"e_1_3_1_7_2","article-title":"Fuzzy knowledge-based modeling and statistical regression in abrasive wood machining","volume":"54","author":"Carrano A.L.","year":"2004","unstructured":"CarranoA.L., TaylorJ.B., YoungR.E., LemasterR.L. and SaloniD.E., Fuzzy knowledge-based modeling and statistical regression in abrasive wood machining, Forest Products Journal 54 (5) (2004).","journal-title":"Forest Products Journal"},{"key":"e_1_3_1_8_2","unstructured":"DhanyaC.T. KumarD.N. Tumkur India ISBN: 978-0-412-7-9 Proceedings of 4th Indian International Conference on Artificial Intelligence (2009) pp. 1299\u20131309."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1515\/JISYS.2009.18.3.193"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0477(1992)073<0049:NPCAN>2.0.CO;2"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"FriedmanJ.H. Multivariate adaptive regression splines The Annals of Statistics (1991) 1\u201367.","DOI":"10.1214\/aos\/1176347963"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1029\/2004GL019733"},{"issue":"2","key":"e_1_3_1_13_2","first-page":"182","article-title":"Monsoon variability: Links to major oscillations over the equatorial Pacific and Indian oceans","volume":"93","author":"Gadgil S.","year":"2007","unstructured":"GadgilS., RajeevanM. and FrancisP.A., Monsoon variability: Links to major oscillations over the equatorial Pacific and Indian oceans, Current Science 93 (2) (2007), 182\u2013194.","journal-title":"Current Science"},{"key":"e_1_3_1_14_2","unstructured":"GalT. BechtelB. and LelovicsE. Comparison of two different Local Climate Zone mapping methods Toulouse 9th International Conference on Urban Climate 2015."},{"key":"e_1_3_1_15_2","unstructured":"HansenB. and RiordanD. Fuzzy case-based prediction of cloud ceiling and visibility In American Meteorological Society 3rd Conference on Artificial Intelligence Applications to the Environmental Science 2003."},{"key":"e_1_3_1_16_2","volume-title":"Classification and modeling with linguistic information granules: Advanced approaches to linguistic Data Mining","author":"Ishibuchi H.","year":"2006","unstructured":"IshibuchiH., NakashimaT., NiiM., Classification and modeling with linguistic information granules: Advanced approaches to linguistic Data Mining, Springer Science & Business Media, 2006."},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Hamilton-WrightA. and StashukD.W. Constructing a fuzzy rule based classification system using pattern discovery In NAFIPS 2005- 2005 Annual Meeting of the North American Fuzzy Information Processing Society (2005) pp. 460\u2013465.","DOI":"10.1109\/NAFIPS.2005.1548579"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1175\/2007WAF2006017.1"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2008.07.015"},{"issue":"6","key":"e_1_3_1_20_2","first-page":"397","article-title":"Rainfall forecasting using data mining technique","volume":"2","author":"Kannan M.","year":"2010","unstructured":"KannanM., PrabhakaranS. and RamachandranP., Rainfall forecasting using data mining technique, Int J Eng Technol 2 (6) (2010), 397\u2013401.","journal-title":"Int J Eng Technol"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0442(2000)013<0579:IMEROI>2.0.CO;2"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1002\/joc.625"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-05285-3_10"},{"key":"e_1_3_1_24_2","unstructured":"KaramouzM. ZahraieB. and EghdamiradS. Seasonal rainfall forecasting using meteorological signals In Proceedings of the 1st Conference of Iran Water Sources Management (2004) pp. 15\u201316."},{"issue":"1","key":"e_1_3_1_25_2","article-title":"A statistical model for seasonal rainfall forecasting over the highlands of Eritrea","volume":"19","author":"Mebrhatu M.T.","year":"2007","unstructured":"MebrhatuM.T., TsuboM. and WalkerS., A statistical model for seasonal rainfall forecasting over the highlands of Eritrea, Discovery and Innovation 19, (1:37)(2007).","journal-title":"Discovery and Innovation"},{"key":"e_1_3_1_26_2","unstructured":"MitraA.K. MeenaL.R. and GiriR.K. Forecasting Of Temperature-Humidity Index using Fuzzy Logic Approach In National Conference On Advances In Mechanical Engineering (2006) pp. 20\u201321."},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12524-008-0025-z"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosres.2010.05.004"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2014.2322385"},{"key":"e_1_3_1_30_2","unstructured":"Understanding Sea Level: Causes. Retrieved from https:\/\/sealevel.gov\/understanding-sea-level\/causes\/overview."},{"issue":"10","key":"e_1_3_1_31_2","first-page":"1380","article-title":"Predicting the extremes of Indian summer monsoon rainfall with coupled ocean-atmosphere models","volume":"104","author":"Nanjundiah R.S.","year":"2013","unstructured":"NanjundiahR.S., FrancisP.A., VedM. and GadgilS., Predicting the extremes of Indian summer monsoon rainfall with coupled ocean-atmosphere models, Current Science 104 (10) (2013), 1380\u20131393.","journal-title":"Current Science"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00382-006-0197-6"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02918713"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.3173\/air.15.331"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1002\/joc.1144"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1623\/hysj.53.6.1165"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-014-1486-8"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-002-0232-4"},{"issue":"10","key":"e_1_3_1_39_2","first-page":"500","article-title":"Empirical statistical modeling of rainfall prediction over Myanmar","volume":"2","author":"Zaw W.T.","year":"2008","unstructured":"ZawW.T. and NaingT.T., Empirical statistical modeling of rainfall prediction over Myanmar, World Academy of Science, Engineering and Technology 2 (10) (2008), 500\u2013504.","journal-title":"World Academy of Science, Engineering and Technology"},{"key":"e_1_3_1_40_2","volume-title":"Introduction to Data Mining","author":"Tan P.","year":"2006","unstructured":"TanP., SteinbachM., KumarV., Introduction to Data Mining, 2006 Addison-Wesley, Boston, MA."},{"key":"e_1_3_1_41_2","unstructured":"HanJ. KamberM. and PeiJ. Data Mining: Concepts and Techniques 3rd edition Morgan Kaufmann 2011."},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.3390\/en9060409"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-017-1615-8"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00382-016-3028-4"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00382-015-2755-2"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.2166\/hydro.2011.044"},{"issue":"2","key":"e_1_3_1_47_2","first-page":"1805","article-title":"A new approach of expert system for rainfall prediction based on data series","volume":"3","author":"Indrabayu N.H.","year":"2013","unstructured":"IndrabayuN.H., PalluM.S. and AchmadA., A new approach of expert system for rainfall prediction based on data series, International Journal of Engineering Research and Applications (3) (2) (2013), 1805\u20131809.","journal-title":"International Journal of Engineering Research and Applications"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-171325","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-171325","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-171325","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:39:39Z","timestamp":1777455579000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-171325"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,9]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,7,27]]}},"alternative-id":["10.3233\/JIFS-171325"],"URL":"https:\/\/doi.org\/10.3233\/jifs-171325","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,9]]}}}