Vathsala, H.Koolagudi, S.G.2026-02-042023IETE Journal of Research, 2023, 69, 6, pp. 3357-33673772063https://doi.org/10.1080/03772063.2021.1912648https://idr.nitk.ac.in/handle/123456789/22241In this paper, we discuss an approach that predicts the quantitative value of rainfall. The proposed algorithm uses a combination of data mining and neuro-fuzzy inference system for prediction. The model is demonstrated on north interior Karnataka (a state in India) rainfall data as a case study. This model is applicable to any geographical area provided apt predictors are included. For north interior Karnataka rainfall prediction predictors are derived from local and global climate conditions. The local condition variables are derived from the mean sea level pressure, temperature, and wind speed in south India. The global variables affecting the north interior Karnataka rainfall include, Darwin sea level pressure, the ENSO indices and southern oscillation. The data mining technique, association rule mining, is used to study the correlation among the predictors; clustering is used for predictor selection as well as membership function creation for fuzzyfication. Neuro-fuzzy inference system is further used for fine tuning the “If-then” rules and crisp value prediction of the rainfall. The prediction accuracy is observed to be good considering Tropical Meteorological Department data. © 2023 IETE.Atmospheric pressureData miningForecastingFuzzy inferenceFuzzy neural networksFuzzy systemsMembership functionsRainSoft computingWindCase-studiesCluster membershipsDense datasetKarnatakaNeuro-fuzzy inference systemsNeuro-fuzzy modelingQuantitative valuesRainfall dataRainfall predictionSoft computing approachesAssociation rules