Assessing flood risk using L-Moments: an analysis of the generalized logistic distribution and the generalized extreme value distribution at Sayong River Station / Nur Diana Zamani ... [et al.]
This study examines severe flood events at Sayong River Station by conducting a Flood Frequency Analysis using the Generalized Logistic (GLO) and Generalized Extreme Value (GEV) distributions. The L-moment approach is utilized for parameter estimation, with quantile estimates assessed for return per...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Universiti Teknologi MARA, Perak
2024
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/106662/1/106662.pdf https://ir.uitm.edu.my/id/eprint/106662/ http://www.mijuitmjournal.com/ |
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| Summary: | This study examines severe flood events at Sayong River Station by conducting a Flood Frequency Analysis using the Generalized Logistic (GLO) and Generalized Extreme Value (GEV) distributions. The L-moment approach is utilized for parameter estimation, with quantile estimates assessed for return periods of 10, 50, and 100 years. A comprehensive comparison of statistical performance indicators, such as RMSE, MAE, and MAPE, was performed to identify the best realistic model for depicting severe flood behavior. The findings indicate that the GLO distribution consistently outperforms the GEV distribution in all criteria. The GLO distribution demonstrated superior performance with a lower RMSE (17.7369), MAE (8.6608), and MAPE (11.83%) relative to the GEV distribution, which exhibited an RMSE of 17.8034, MAE of 8.7957, and MAPE of 12.98%. These findings validate the GLO distribution as the better appropriate model for representing peak streamflow data. Moreover, quantile estimates obtained from the GLO distribution are197.3153 m³/s for the 10-year, 363.8308 m³/s for the 50-year and 469.9711 m³/s for the 100-year return periods. The GLO distribution exhibit greater concordance with empirical data, further validating its accuracy. The superior performance of the GLO distribution emphasizes the importance of selecting the appropriate distribution for flood risk assessment. The GLO distribution yields more accurate predictions of severe flood magnitudes, hence enhancing flood estimations, infrastructure design, and mitigation measures at Sayong River Station. |
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