| description abstract | This study attempts to predict the maximum temperature, along with the number of heat wave days (HWDs) at lead times of 7 and 15 days over Rajasthan, i.e., a semiarid region, using four machine learning (ML) algorithms, namely, artificial neural networks (ANN), support vector regression (SVR), random forest (RF), and eXtreme gradient boosting (XGBoost). It uses five key atmospheric variables, i.e., air temperature, geopotential height, relative humidity, U-wind, and V-wind, to predict the daily maximum temperatures for the months of April, May, and June, for the period from 1991 to 2020. The ML models are developed by using spatially averaged atmospheric variables and daily maximum temperature. The study demonstrates decent accuracy in forecasting the total annual count of HWDs and daily maximum temperature for Rajasthan for the 7-day lead time. However, as the lead time extends to 15 days, the model performances experience a decline. While comparing the performances of the four models, the SVR outperforms ANN, RF, and XGBoost in prediction. The findings of the current study indicate the potential utility of spatiotemporal dynamics in meteorological variables for long-term heat wave prediction. Moreover, the successful performances of the considered models, i.e., ANN, SVR, RF, and XGBoost, exhibit substantial future potential for reliable application in this context. | |
