Drought Forecasting by SPI Index and ANFIS Model Using Fuzzy C-mean Clustering

Drought is the interaction between environment and water cycle in the world and affects natural environment of an area when it persists for a longer period. So، developing a suitable index to forecast the spatial and temporal distribution of drought plays an important role in the planning and management of natural resources and water resource systems. In this article، firstly، the drought concept and drought indexes were introduced and then the fuzzy neural networks and fuzzy C-mean clustering were applied to forecast drought via standardized precipitation index (SPI). The results of this research indicate that the SPI index is more capable than the other indexes such as PDSI (Palmer Drought Severity Index)، PAI (Palfai Aridity Index) and etc. in drought forecasting process. Moreover، application of adaptive nero-fuzzy network accomplished by C-mean clustering has high efficiency in the drought forecasting.