Evaluating the Performance of ANN, SVR, RF, and XGBoost in the Prediction of Maximum Temperature and Heat Wave Days over Rajasthan, IndiaSource: Journal of Hydrologic Engineering:;2024:;Volume ( 029 ):;issue: 006::page 04024044-1DOI: 10.1061/JHYEFF.HEENG-6243Publisher: American Society of Civil Engineers
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.
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contributor author | Srikanth Bhoopathi | |
contributor author | K. Sumanth | |
contributor author | L. Akanksha | |
contributor author | Manali Pal | |
date accessioned | 2025-04-20T10:20:33Z | |
date available | 2025-04-20T10:20:33Z | |
date copyright | 9/25/2024 12:00:00 AM | |
date issued | 2024 | |
identifier other | JHYEFF.HEENG-6243.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4304514 | |
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. | |
publisher | American Society of Civil Engineers | |
title | Evaluating the Performance of ANN, SVR, RF, and XGBoost in the Prediction of Maximum Temperature and Heat Wave Days over Rajasthan, India | |
type | Journal Article | |
journal volume | 29 | |
journal issue | 6 | |
journal title | Journal of Hydrologic Engineering | |
identifier doi | 10.1061/JHYEFF.HEENG-6243 | |
journal fristpage | 04024044-1 | |
journal lastpage | 04024044-16 | |
page | 16 | |
tree | Journal of Hydrologic Engineering:;2024:;Volume ( 029 ):;issue: 006 | |
contenttype | Fulltext |