Faculty Publications
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Item Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches(Elsevier Ltd, 2017) Vathsala, H.; Koolagudi, S.G.In this paper we discuss a data mining application for predicting peninsular Indian summer monsoon rainfall, and propose an algorithm that combine data mining and statistical techniques. We select likely predictors based on association rules that have the highest confidence levels. We then cluster the selected predictors to reduce their dimensions and use cluster membership values for classification. We derive the predictors from local conditions in southern India, including mean sea level pressure, wind speed, and maximum and minimum temperatures. The global condition variables include southern oscillation and Indian Ocean dipole conditions. The algorithm predicts rainfall in five categories: Flood, Excess, Normal, Deficit and Drought. We use closed itemset mining, cluster membership calculations and a multilayer perceptron function in the algorithm to predict monsoon rainfall in peninsular India. Using Indian Institute of Tropical Meteorology data, we found the prediction accuracy of our proposed approach to be exceptionally good. © 2016 Elsevier LtdItem Neuro-Fuzzy Model for Quantified Rainfall Prediction Using Data Mining and Soft Computing Approaches(Taylor and Francis Ltd., 2023) Vathsala, H.; Koolagudi, S.G.In 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.