A new soft computing model for daily streamflow forecasting
Climate change; Data streams; Floods; Forecasting; Genetic algorithms; Hydroelectric power plants; Mean square error; Multilayer neural networks; Particle swarm optimization (PSO); Soft computing; Soil conservation; Water conservation; Water management; Water supply; Flow quantification; Multi layer...
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2023
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my.uniten.dspace-258912023-05-29T17:05:27Z A new soft computing model for daily streamflow forecasting Sammen S.S. Ehteram M. Abba S.I. Abdulkadir R.A. Ahmed A.N. El-Shafie A. 57192093108 57113510800 57208942739 57200567560 57214837520 16068189400 Climate change; Data streams; Floods; Forecasting; Genetic algorithms; Hydroelectric power plants; Mean square error; Multilayer neural networks; Particle swarm optimization (PSO); Soft computing; Soil conservation; Water conservation; Water management; Water supply; Flow quantification; Multi layer perceptron; Optimization algorithms; Predicting models; Root mean square errors; Soft computing models; Streamflow forecasting; Watershed management; Stream flow; algorithm; forecasting method; hydroelectric power plant; numerical model; optimization; principal component analysis; streamflow; watershed; Helianthus Accurate stream flow quantification and prediction are essential for the local and global planning and management of basins to cope with climate change. The ability to forecast streamflow is crucial, as it can help mitigate flood risks. Long-term stream flow data records are needed for hydropower plant construction, flood prediction, watershed management, and long-term water supply use. An accurate assessment of streamflow is considered as�very challenging and critical tasks. A new predicting model is developed in this research, combining the technique of sunflower optimization (SFA) as an evolutionary algorithm with the multi-layer perceptron (MLP) algorithm to predict streamflow in Malaysia's Jam Seyed Omar (JSO) and Muda Di Jeniang (MDJ) stations. Principal component analysis (PCA) was performed on Q (t) (t: the number of the current day) before model creation to pick essential inputs for a maximum of 6 lags. With the classical MLP and two other hybrid MLP models (MLP-particle swarm optimization (MLP-PSO) and MLP-genetic algorithm (MLP-GA)), the results of the MLP-sunflower algorithm (SFA) were benchmarked. As compared to other models, the MLP-SFA could be able to reduce the Root Mean Square Error (RMSE) by a value of between 12 and 21% at the JSO station and between 8 and 24% at the MDJ station. In conclusion, this research found that combining MLP with optimization algorithms improved the precision of the stand-alone MLP model, with SFA integration being the most efficient. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. Final 2023-05-29T09:05:27Z 2023-05-29T09:05:27Z 2021 Article 10.1007/s00477-021-02012-1 2-s2.0-85105878406 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105878406&doi=10.1007%2fs00477-021-02012-1&partnerID=40&md5=de2bfa0995b71148196efb238552136f https://irepository.uniten.edu.my/handle/123456789/25891 35 12 2479 2491 Springer Science and Business Media Deutschland GmbH Scopus |
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Climate change; Data streams; Floods; Forecasting; Genetic algorithms; Hydroelectric power plants; Mean square error; Multilayer neural networks; Particle swarm optimization (PSO); Soft computing; Soil conservation; Water conservation; Water management; Water supply; Flow quantification; Multi layer perceptron; Optimization algorithms; Predicting models; Root mean square errors; Soft computing models; Streamflow forecasting; Watershed management; Stream flow; algorithm; forecasting method; hydroelectric power plant; numerical model; optimization; principal component analysis; streamflow; watershed; Helianthus |
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57192093108 Sammen S.S. Ehteram M. Abba S.I. Abdulkadir R.A. Ahmed A.N. El-Shafie A. |
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Article |
| author |
Sammen S.S. Ehteram M. Abba S.I. Abdulkadir R.A. Ahmed A.N. El-Shafie A. |
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Sammen S.S. Ehteram M. Abba S.I. Abdulkadir R.A. Ahmed A.N. El-Shafie A. A new soft computing model for daily streamflow forecasting |
| author_sort |
Sammen S.S. |
| title |
A new soft computing model for daily streamflow forecasting |
| title_short |
A new soft computing model for daily streamflow forecasting |
| title_full |
A new soft computing model for daily streamflow forecasting |
| title_fullStr |
A new soft computing model for daily streamflow forecasting |
| title_full_unstemmed |
A new soft computing model for daily streamflow forecasting |
| title_sort |
new soft computing model for daily streamflow forecasting |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2023 |
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1806423998504894464 |
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13.214268 |