Abstract <p>This study proposes the improvement of an adaptive parameter estimation approach for the GEVD using multi-output machine learning for the non-stationary models and comparing it to maximum likelihood estimation for the stationary models. This method effectively estimates GEVD parameters, improving extreme value analysis. In order to forecast the return level of extreme rainfall in Northeast Thailand, which is a risk of flooding due to the huge amount of rainfall, the initial step is to identify the key variables that are used to estimate the three GEVD parameters: location, scale, and shape parameters (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12202_2025_8167_Article_IEq1.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\mu\)</EquationSource>  </InlineEquation>, <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12202_2025_8167_Article_IEq2.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">\(\sigma\)</EquationSource>  </InlineEquation>, and <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12202_2025_8167_Article_IEq3.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">\(\xi\)</EquationSource>  </InlineEquation>). All features can be accomplished by estimating the correlation coefficients and using them to calculate the parameters. The information was gathered from meteorological and satellite data in Northeast Thailand between 2012 and 2023. It includes various variables such as rainfall, climate, Normalized difference vegetation index (NDVI), and runoff. The data was compiled from the Meteorological Department of Thailand and 322 meteorological stations. The evaluation performance and accuracy of the model are compared. Finally, two-dimensional maps depicting return levels for various return periods (2, 5, 10, 20, 50, and 100 years) are being made available for future use. This study enhances parameter estimation for flood risk mitigation and water resource management.</p>

Multi-Output Parameter Estimation of the Generalized Extreme Value Distribution for Flood Risk in Northeast Thailand

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发表日期：

2025年1月1日

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Rapeeporn Chamchong,
Tossapol Phoophiwfa,
Sujitta Suraphee,
Witchaya Rattanamethawee,
Andrei Volodin,
Piyapatr Busababodhin

摘要

Abstract

This study proposes the improvement of an adaptive parameter estimation approach for the GEVD using multi-output machine learning for the non-stationary models and comparing it to maximum likelihood estimation for the stationary models. This method effectively estimates GEVD parameters, improving extreme value analysis. In order to forecast the return level of extreme rainfall in Northeast Thailand, which is a risk of flooding due to the huge amount of rainfall, the initial step is to identify the key variables that are used to estimate the three GEVD parameters: location, scale, and shape parameters ( \(\mu\) , \(\sigma\) , and \(\xi\) ). All features can be accomplished by estimating the correlation coefficients and using them to calculate the parameters. The information was gathered from meteorological and satellite data in Northeast Thailand between 2012 and 2023. It includes various variables such as rainfall, climate, Normalized difference vegetation index (NDVI), and runoff. The data was compiled from the Meteorological Department of Thailand and 322 meteorological stations. The evaluation performance and accuracy of the model are compared. Finally, two-dimensional maps depicting return levels for various return periods (2, 5, 10, 20, 50, and 100 years) are being made available for future use. This study enhances parameter estimation for flood risk mitigation and water resource management.