Abstract

Drought -as an environmental occurrence, is an essential part of climatic variability. Droughts are the product of keen water shortage causing severe and sometimes disastrous economic and social consequences. The Standardized Precipitation Index (SPI) provides the forecasting of drought. The SPI also detects moisture shortage more rapidly which has a reply time scale of approximately 3, 6, 12, 24, 48 months. On the other hand, fuzzy logic can be used to focus on modeling problems characterized by imprecise or ambiguous information. It also needs a complete understanding of the drought causing factors, severity classification and to interpret the drought forecasted output variables. In this study, the number of linguistic terms referred to as fuzzy sets, is assign to variable rainfall. The degree of membership (from 0 to 1) of a real valued input (SPI) to a particular fuzzy set A (ED, SD, MD, N, MW, SW, EW) is specified by a membership function ( ) A μ x . Fuzzification of linguistic variables is classified into linguistic labels by transfer membership functions for each of the variable. The drought forecasting measures in the form of fuzzy ranks were identified for each year. All the fuzzified ranks were identified for each SPI values. The fuzzified ranks were used for forecasting the drought severity class for different years. In this study the fuzzy logic based drought forecasting method using SPI can be successively used for drought analysis, forecasting and to mitigate unexpected meteorological problems.