L-Moment methodology for flood frequency analysis: a case study of Triang River

This study investigates flood frequency analysis in Peninsular Malaysia using extreme value distributions derived from the L-Moment method. This study evaluates the relevance of three probability distributions: Generalized Extreme Value (GEV), Generalized Pareto (GPA), and Generalized Logistic (GLO), for modelling flood magnitudes. The research focuses on parameter estimation, performance evaluation, and quantile estimation to assess flood risks. Utilizing hydrological data from 1961 to 2023, the research employs the goodness-of-fit tests alongside performance metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Root Mean Square Percentage Error (RMSPE), and Coefficient of Determination (R2).The results reveal that the GEV distribution consistently outperforms GLO and GPA distributions, achieving superior accuracy across metrics and reliable quantile estimates for different return periods. The findings contribute to the field of hydrology by offering robust models for flood risk analysis and practical strategies for mitigation. Policymakers and urban planners could leverage these insights to improve disaster management and infrastructure planning in flood-prone areas of Peninsular Malaysia, addressing the challenges posed by persistent monsoonal floods.