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    Evaluating the Performance of ANN, SVR, RF, and XGBoost in the Prediction of Maximum Temperature and Heat Wave Days over Rajasthan, India

    Source: Journal of Hydrologic Engineering:;2024:;Volume ( 029 ):;issue: 006::page 04024044-1
    Author:
    Srikanth Bhoopathi
    ,
    K. Sumanth
    ,
    L. Akanksha
    ,
    Manali Pal
    DOI: 10.1061/JHYEFF.HEENG-6243
    Publisher: 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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      Evaluating the Performance of ANN, SVR, RF, and XGBoost in the Prediction of Maximum Temperature and Heat Wave Days over Rajasthan, India

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4304514
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    contributor authorSrikanth Bhoopathi
    contributor authorK. Sumanth
    contributor authorL. Akanksha
    contributor authorManali Pal
    date accessioned2025-04-20T10:20:33Z
    date available2025-04-20T10:20:33Z
    date copyright9/25/2024 12:00:00 AM
    date issued2024
    identifier otherJHYEFF.HEENG-6243.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304514
    description abstractThis 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.
    publisherAmerican Society of Civil Engineers
    titleEvaluating the Performance of ANN, SVR, RF, and XGBoost in the Prediction of Maximum Temperature and Heat Wave Days over Rajasthan, India
    typeJournal Article
    journal volume29
    journal issue6
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/JHYEFF.HEENG-6243
    journal fristpage04024044-1
    journal lastpage04024044-16
    page16
    treeJournal of Hydrologic Engineering:;2024:;Volume ( 029 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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