Prediction of maximum air temperature for defining heat wave in Rajasthan and Karnataka states of India using machine learning approach

Abstract

Temperature rise, associated with global warming, has increased the severity and frequency of heat waves around the world. Rajasthan and Karnataka are two major states of India and these have been facing frequent heat waves in the past few years. The present study used machine learning approach to predict the maximum air temperature (AT) for defining the heat wave occurrences in these two states. The analysis was based on the monthly data of 13 parameters, collected from NASA's Giovanni and ERA5 reanalysis data during the summer season for the past 10-years (2013–2022). The data obtained were at different resolutions, which was resampled to 5 km × 5 km spatial resolution. Pearson's correlation and sensitivity analysis were used to check the dominant input parameters. Three machine learning approaches were used in the study: multiple linear regression (MLR), support vector regression (SVR), and random forest (RF). 3-fold cross validation was used to evaluate the model performance. The data was divided into three periods for the purpose of testing, training and validation. It was observed that the maximum AT in both states was above the limits of IMD criteria for defining heat wave occurrences. The performance of the models was evaluated using statistical metrics, comprising of root mean square error (RMSE) and coefficient of determination (R2). Good correlation of AT with land surface temperature (LST), black carbon (BC), AOD, and CO was found for both the states. AT was found to be more sensitive to the change in LST and BC compared to other parameters. The machine learning results indicated that RF outperformed MLR and SVR in predicting AT in both states. The adjusted R2 was 0.90 and 0.92, for Rajasthan and Karnataka, respectively while RMSE was 2.36% and 1.44%, for Rajasthan and Karnataka, respectively. Overall, the study shows that machine learning-based approaches can predict maximum AT for defining heat wave conditions with high accuracy. The study can have significantly applications in different fields like climate modelling studies, urban planning and infrastructure, agriculture etc. and it can help to implement appropriate measures to mitigate the adverse impacts of temperature rise.