Data Mining for Evolving Fuzzy Association Rules for Predicting Monsoon Rainfall of India

摘要

We used a data mining algorithm to evolve fuzzy association rules between the atmospheric indices and the Summer Monsoon Rainfall of All-India and two homogenous regions (Peninsular and West central). El Nino and Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation zonal wind index (EQWIN) indices are used as the causative variables. Rules extracted are showing a negative relation with ENSO index and a positive relation with the EQWIN index. A fuzzy rule based prediction technique is also implemented on the same indices to predict the summer monsoon rainfall of All-India, Peninsular, and West central regions. Rules are defined using a training dataset for the period 1958-1999 and validated for the period 2000-2006. The fuzzy outputs of the defined rules are converted into crisp outputs using the weighted counting algorithm. The variability of the summer monsoon rainfall over the years is well captured by this technique, thus proving to be efficient even when the linear statistical relation between the indices is weak.