APPLICATION OF NEURAL NETWORK IN DROUGHT FORECASTING; AN INTENSE LITERATURE REVIEW

India is the agrarian country. The overall economy of our country is based on agriculture. Although the methods of cultivation are traditional and not hi-tech thus more over 75% of our farmers are dependent on monsoon. Prediction of actual monsoon is a challenge for meteorological scientists. Since the climatic data time series shows highly non-linear and chaotic behavior thus its forecast is still an enigma. Thus, forecasting of climate phenomenon is a challenging issue for the researchers round the globe. However, it is a prime necessity to forecast climatic changes such as Rainfall (daily rainfall, monthly rainfall, heavy rainfall etc.), Flood, Drought, minimum and maximum Temperature, River flow etc. To recognize applications of Artificial Neural Network (ANNs) in weather forecasting, especially in drought forecasting a comprehensive literature review from 2000 to 2017 is done and presented in this paper. In the study, more over 90 contributions have been surveyed and it has been observed that the architecture of ANN such as BPN, RBFN, MLP, ANFIS, ARIMA etc. are found best to forecast chaotic behavior and have efficient enough to forecast drought as well as other weather phenomenon over broader or smaller homogeneous region.