Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, Pakistan
Floods are among the major natural disasters that cause loss of life and economic damage worldwide. Floods damage homes, crops, roads, and basic infrastructure, forcing people to migrate from high flood-risk areas. However, due to a lack of information about the effective variables in forecasting, t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/21/3533 |
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| author | Fahad Ahmed Ho Huu Loc Edward Park Muhammad Hassan Panuwat Joyklad |
| author_facet | Fahad Ahmed Ho Huu Loc Edward Park Muhammad Hassan Panuwat Joyklad |
| author_sort | Fahad Ahmed |
| collection | DOAJ |
| description | Floods are among the major natural disasters that cause loss of life and economic damage worldwide. Floods damage homes, crops, roads, and basic infrastructure, forcing people to migrate from high flood-risk areas. However, due to a lack of information about the effective variables in forecasting, the development of an accurate flood forecasting system remains difficult. The flooding process is quite complex as it has a nonlinear relationship with various meteorological and topographic parameters. Therefore, there is always a need to develop regional models that could be used effectively for water resource management in a particular locality. This study aims to establish and evaluate various data-driven flood forecasting models in the Jhelum River, Punjab, Pakistan. The performance of Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR), Two Layer Back Propagation (TLBP), Conjugate Gradient (CG), and Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based ANN models were evaluated using R<sup>2</sup>, variance, bias, RMSE and MSE. The R<sup>2</sup>, bias, and RMSE values of the best-performing LLR model were 0.908, 0.009205, and 1.018017 for training and 0.831, −0.05344, and 0.919695 for testing. Overall, the LLR model performed best for both the training and validation periods and can be used for the prediction of floods in the Jhelum River. Moreover, the model provides a baseline to develop an early warning system for floods in the study area. |
| first_indexed | 2024-03-09T18:33:19Z |
| format | Article |
| id | doaj.art-823e30ed344c4ca48a77613068213603 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T18:33:19Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-823e30ed344c4ca48a776130682136032023-11-24T07:20:59ZengMDPI AGWater2073-44412022-11-011421353310.3390/w14213533Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, PakistanFahad Ahmed0Ho Huu Loc1Edward Park2Muhammad Hassan3Panuwat Joyklad4Water Engineering and Management, Asian Institute of Technology (AIT), Pathum Thani 12120, ThailandWater Engineering and Management, Asian Institute of Technology (AIT), Pathum Thani 12120, ThailandNational Institute of Education (NIE) and Earth Observatory of Singapore (EOS), Nanyang Technological University (NTU), Singapore 637616, SingaporeDepartment of Civil Engineering, Mirpur University of Science and Technology, Mirpur 10250, PakistanDepartment of Civil and Environmental Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok 26120, ThailandFloods are among the major natural disasters that cause loss of life and economic damage worldwide. Floods damage homes, crops, roads, and basic infrastructure, forcing people to migrate from high flood-risk areas. However, due to a lack of information about the effective variables in forecasting, the development of an accurate flood forecasting system remains difficult. The flooding process is quite complex as it has a nonlinear relationship with various meteorological and topographic parameters. Therefore, there is always a need to develop regional models that could be used effectively for water resource management in a particular locality. This study aims to establish and evaluate various data-driven flood forecasting models in the Jhelum River, Punjab, Pakistan. The performance of Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR), Two Layer Back Propagation (TLBP), Conjugate Gradient (CG), and Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based ANN models were evaluated using R<sup>2</sup>, variance, bias, RMSE and MSE. The R<sup>2</sup>, bias, and RMSE values of the best-performing LLR model were 0.908, 0.009205, and 1.018017 for training and 0.831, −0.05344, and 0.919695 for testing. Overall, the LLR model performed best for both the training and validation periods and can be used for the prediction of floods in the Jhelum River. Moreover, the model provides a baseline to develop an early warning system for floods in the study area.https://www.mdpi.com/2073-4441/14/21/3533ANNflood forecastingflood modelingJhelum River |
| spellingShingle | Fahad Ahmed Ho Huu Loc Edward Park Muhammad Hassan Panuwat Joyklad Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, Pakistan Water ANN flood forecasting flood modeling Jhelum River |
| title | Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, Pakistan |
| title_full | Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, Pakistan |
| title_fullStr | Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, Pakistan |
| title_full_unstemmed | Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, Pakistan |
| title_short | Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, Pakistan |
| title_sort | comparison of different artificial intelligence techniques to predict floods in jhelum river pakistan |
| topic | ANN flood forecasting flood modeling Jhelum River |
| url | https://www.mdpi.com/2073-4441/14/21/3533 |
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