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Abstract <p>Drought indices are pivotal for comprehending and monitoring water scarcity in Northeast Thailand. Researchers precisely assess these indices, considering local climate conditions, geographical features, impacts on vegetation and agriculture, hydrological considerations, and the temporal and spatial scales of drought events. The reliability of these assessments depends on thorough validation against ground data, encompassing rainfall records and soil moisture measurements. This study explores the integration of various indices to enhance the overall comprehensiveness of drought assessments in the Mun watershed. It contributes to the field by evaluating drought conditions across the entire watershed, utilizing meteorological, soil moisture, and hydrological drought indicators. These indicators encompass the standardized runoff index, standardized precipitation index, standardized soil moisture index, and standardized precipitation evapotranspiration index. In order to establish a comprehensive multivariate drought index, this study employs ensemble learning, incorporating boosting techniques such as boosting, AdaBoost (adaptive boosting), and XGBoost (extreme gradient boosting). The performance of each model is assessed through comparisons using RMSE, MAE, and <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12202_2025_8201_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\(R^{2}\)</EquationSource> <!--LobJMat2560495Phoophiwfa-m1--> </InlineEquation>.</p>

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Ensemble Machine Learning for Comprehensive Drought Assessment: A Case Study in the Mun Watershed of Northeast Thailand

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Tossapol Phoophiwfa,
  • Prapawan Chomphuwiset,
  • Thanawan Prahadchai,
  • Sujitta Suraphee,
  • Andrei Volodin,
  • Piyapatr Busababodhin

摘要

Abstract

Drought indices are pivotal for comprehending and monitoring water scarcity in Northeast Thailand. Researchers precisely assess these indices, considering local climate conditions, geographical features, impacts on vegetation and agriculture, hydrological considerations, and the temporal and spatial scales of drought events. The reliability of these assessments depends on thorough validation against ground data, encompassing rainfall records and soil moisture measurements. This study explores the integration of various indices to enhance the overall comprehensiveness of drought assessments in the Mun watershed. It contributes to the field by evaluating drought conditions across the entire watershed, utilizing meteorological, soil moisture, and hydrological drought indicators. These indicators encompass the standardized runoff index, standardized precipitation index, standardized soil moisture index, and standardized precipitation evapotranspiration index. In order to establish a comprehensive multivariate drought index, this study employs ensemble learning, incorporating boosting techniques such as boosting, AdaBoost (adaptive boosting), and XGBoost (extreme gradient boosting). The performance of each model is assessed through comparisons using RMSE, MAE, and \(R^{2}\) .