Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques
In this paper, the evaluation of the performance of five flood prediction models in the Simineh-Rood River, Lake Urmia basin, Iran, is discussed in detail. To this purpose, the performance of Transfer Function, Saint-Venant equations, Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System,...
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University of Birjand
2024-09-01
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| Series: | Water Harvesting Research |
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| Online Access: | https://jwhr.birjand.ac.ir/article_3050_96af733f3a28d6923ddd58e66d18cfeb.pdf |
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| author | Jafar Chabokpour |
| author_facet | Jafar Chabokpour |
| author_sort | Jafar Chabokpour |
| collection | DOAJ |
| description | In this paper, the evaluation of the performance of five flood prediction models in the Simineh-Rood River, Lake Urmia basin, Iran, is discussed in detail. To this purpose, the performance of Transfer Function, Saint-Venant equations, Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Support Vector Machine models are evaluated for 2018 and 2019 flood data. Specifically, the models are rated according to their accuracy, computational efficiency, and robustness under different flow regimes and at various forecast times. This now leads to a maximum Nash-Sutcliffe Efficiency of 0.91 for the Saint-Venant equations during the 2019 flood event, followed by ANN with 0.89, ANFIS with 0.87, SVM with 0.85, and lastly, Transfer Function with 0.78. The same is the case for peak flow discharge, which was best predicted by the Saint-Venant model to be 193.80 m³/s while the observed value was 200.83 m³/s. This model maintained its consistency with respect to low, medium, and high flows, where the values of NSE were 0.89, 0.92, and 0.91, respectively. However, compared to the other models, which took 0.5–8 s, it had a much larger computational time, 120 s for a 72-h simulation. The sensitivity analysis returned variable model responses to the quality of the input data; an input variation of 20% reduced the NSE of the Saint-Venant model to 0.73 and that of the Transfer Function to 0.44. This study provides quantitative insight into the choice of flood prediction methods in a semi-arid region, with respect to required accuracy, computational resources, and forecast lead-time. |
| format | Article |
| id | doaj-art-a84a8cf7ff484e8bba15f0fc8369b606 |
| institution | DOAJ |
| issn | 2476-6976 2476-7603 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | University of Birjand |
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| series | Water Harvesting Research |
| spelling | doaj-art-a84a8cf7ff484e8bba15f0fc8369b6062025-08-20T03:11:10ZengUniversity of BirjandWater Harvesting Research2476-69762476-76032024-09-017219420910.22077/jwhr.2024.7966.11473050Comparative Analysis of Transfer Function Method with Advanced Flood Prediction TechniquesJafar Chabokpour0Associate Professor, Department of Civil Engineering, University of Maragheh, Maragheh, Iran.In this paper, the evaluation of the performance of five flood prediction models in the Simineh-Rood River, Lake Urmia basin, Iran, is discussed in detail. To this purpose, the performance of Transfer Function, Saint-Venant equations, Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Support Vector Machine models are evaluated for 2018 and 2019 flood data. Specifically, the models are rated according to their accuracy, computational efficiency, and robustness under different flow regimes and at various forecast times. This now leads to a maximum Nash-Sutcliffe Efficiency of 0.91 for the Saint-Venant equations during the 2019 flood event, followed by ANN with 0.89, ANFIS with 0.87, SVM with 0.85, and lastly, Transfer Function with 0.78. The same is the case for peak flow discharge, which was best predicted by the Saint-Venant model to be 193.80 m³/s while the observed value was 200.83 m³/s. This model maintained its consistency with respect to low, medium, and high flows, where the values of NSE were 0.89, 0.92, and 0.91, respectively. However, compared to the other models, which took 0.5–8 s, it had a much larger computational time, 120 s for a 72-h simulation. The sensitivity analysis returned variable model responses to the quality of the input data; an input variation of 20% reduced the NSE of the Saint-Venant model to 0.73 and that of the Transfer Function to 0.44. This study provides quantitative insight into the choice of flood prediction methods in a semi-arid region, with respect to required accuracy, computational resources, and forecast lead-time.https://jwhr.birjand.ac.ir/article_3050_96af733f3a28d6923ddd58e66d18cfeb.pdfflood predictionlake urmiasoft computing methodstransfer function |
| spellingShingle | Jafar Chabokpour Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques Water Harvesting Research flood prediction lake urmia soft computing methods transfer function |
| title | Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques |
| title_full | Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques |
| title_fullStr | Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques |
| title_full_unstemmed | Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques |
| title_short | Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques |
| title_sort | comparative analysis of transfer function method with advanced flood prediction techniques |
| topic | flood prediction lake urmia soft computing methods transfer function |
| url | https://jwhr.birjand.ac.ir/article_3050_96af733f3a28d6923ddd58e66d18cfeb.pdf |
| work_keys_str_mv | AT jafarchabokpour comparativeanalysisoftransferfunctionmethodwithadvancedfloodpredictiontechniques |