Kikon, A.Deka, P.C.2026-02-062022Lecture Notes in Civil Engineering, 2022, Vol.176, , p. 43-5223662557https://doi.org/10.1007/978-981-16-4629-4_4https://idr.nitk.ac.in/handle/123456789/30074Drought forecasting is one of the crucial tools for the water management system, and understanding the different climatic variables affecting the occurrences of drought is a major scientific challenge. In this study, drought forecasting is done for the Peninsular region of India using different machine learning algorithms. A meteorological drought index known as Standardized Precipitation Index (SPI) which is dependent on the precipitation is taken into account for analysis. The SPI with a different timescale for 3-, 6-, 9-, 12-month were calculated from 1958–2017 for 60 years. SPI is a function of precipitation and the trend of rainfall followed may be found to be similar in some regions. Two different models, GA-ANFIS and GRNN were compared in this study. The results obtained from the statistical performance assessment of the models were compared with each other. For different timescale, there is a variation in its evaluation metrics. Comparing the performance assessment of the two different models, it is noticeable that the performance assessment of the statistics of the GA-ANFIS model outperformed GRNN model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Drought forecastingMachine learning algorithmSPI