Hybrid Model for Impact Analysis of Climate Change on Droughts in Maharashtra State

Abstract

Droughts are prolonged periods of dry weather that have become more frequent and severe due to climate change and global warming. It can have devastating effects on agriculture, water resources, and ecosystems. Hence, a framework for the prediction of droughts is necessary for mitigating its impact, as it enables authorities to prepare and respond effec tively. This paper presents a hybrid model comprised of the Convolutional Neural Network and Gated Recurrent Units (called CNN-GRU) to predict the Standardized Precipitation Evapotranspiration Index (SPEI), which is used to measure drought intensity. We used India Meteorological Department (IMD) rainfall and temperature data of Maharashtra state of India during the years 1960-2021 as the historical dataset. Whereas, for the future pro jections, we used the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset of the same region during the years 2015-2100 for different Shared Socioeconomic Pathways (SSP) scenarios. Both these datasets include the daily precipitation, minimum temperature and maximum temperature values. The proposed model is trained and validated using IMD dataset and the final evaluation of its ability to predict the future droughts is conducted on the CMIP6 dataset. We confirmed that it outperforms in terms of mean squared error, mean absolute error, and root mean squared error over both the IMD and the CMIP6 datasets based on the comparative study with the existing deep learning models.