Data mining for evolution of association rules for droughts and floods in India using climate inputs

Abstract

An accurate prediction of extreme rainfall events can significantly aid in policy making and also in designing an effective risk management system. Frequent occurrences of droughts and floods in the past have severely affected the Indian economy, which depends primarily on agriculture. Data mining is a powerful new technology which helps in extracting hidden predictive information (future trends and behaviors) from large databases and thus allowing decision makers to make proactive knowledge-driven decisions. In this study, a data-mining algorithm making use of the concepts of minimal occurrences with constraints and time lags is used to discover association rules between extreme rainfall events and climatic indices. The algorithm considers only the extreme events as the target episodes (consequents) by separating these from the normal episodes, which are quite frequent, and finds the time-lagged relationships with the climatic indices, which are treated as the antecedents. Association rules are generated for all the five homogenous regions of India and also for All India by making use of the data from 1960 to 1982. The analysis of the rules shows that strong relationships exist between the climatic indices chosen, i.e., Darwin sea level pressure, North Atlantic Oscillation, Nino 3.4 and sea surface temperature values, and the extreme rainfall events. Validation of the rules using data for the period 1983–2005 clearly shows that most of the rules are repeating, and for some rules, even if they are not exactly the same, the combinations of the indices mentioned in these rules are the same during validation period, with slight variations in the classes taken by the indices.