Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm
Water level (WL) forecasting has become a difficult undertaking due to spatiotemporal fluctuations in climatic factors and complex physical processes. This paper proposes a novel hybrid machine learning model based on an artificial neural network (ANN) and the Marine Predators algorithm (MPA) for mo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/6955271 |
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| author | Sarah J. Mohammed Salah L. Zubaidi Nadhir Al-Ansari Hussein Mohammed Ridha Nabeel Saleem Saad Al-Bdairi |
| author_facet | Sarah J. Mohammed Salah L. Zubaidi Nadhir Al-Ansari Hussein Mohammed Ridha Nabeel Saleem Saad Al-Bdairi |
| author_sort | Sarah J. Mohammed |
| collection | DOAJ |
| description | Water level (WL) forecasting has become a difficult undertaking due to spatiotemporal fluctuations in climatic factors and complex physical processes. This paper proposes a novel hybrid machine learning model based on an artificial neural network (ANN) and the Marine Predators algorithm (MPA) for modeling monthly water levels of the Tigris River in Al-Kut, Iraq. Data preprocessing techniques are employed to enhance data quality and determine the optimal input model. Historical data for water level and climatic factors data are utilized from 2011 to 2020 to build and assess the model. MPA-ANN algorithm’s performance is compared with recent constriction coefficient-based particle swarm optimization and chaotic gravitational search algorithm (CPSOCGSA-ANN) and slime mold algorithm (SMA-ANN) to reduce uncertainty and raise the prediction range. The finding demonstrated that singular spectrum analysis is a highly effective method to denoise time series. MPA-ANN outperformed CPSOCGSA-ANN and SMA-ANN algorithms based on different statistical criteria. The suggested novel methodology offers good results with scatter index (SI) = 0.0009 and coefficient of determination (R2 = 0.98). |
| format | Article |
| id | doaj-art-fdc77fd4b87749d4ae23ab12e4452e9d |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-fdc77fd4b87749d4ae23ab12e4452e9d2025-02-03T06:11:52ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/6955271Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators AlgorithmSarah J. Mohammed0Salah L. Zubaidi1Nadhir Al-Ansari2Hussein Mohammed Ridha3Nabeel Saleem Saad Al-Bdairi4Department of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil Environmental and Natural Resources EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Civil EngineeringWater level (WL) forecasting has become a difficult undertaking due to spatiotemporal fluctuations in climatic factors and complex physical processes. This paper proposes a novel hybrid machine learning model based on an artificial neural network (ANN) and the Marine Predators algorithm (MPA) for modeling monthly water levels of the Tigris River in Al-Kut, Iraq. Data preprocessing techniques are employed to enhance data quality and determine the optimal input model. Historical data for water level and climatic factors data are utilized from 2011 to 2020 to build and assess the model. MPA-ANN algorithm’s performance is compared with recent constriction coefficient-based particle swarm optimization and chaotic gravitational search algorithm (CPSOCGSA-ANN) and slime mold algorithm (SMA-ANN) to reduce uncertainty and raise the prediction range. The finding demonstrated that singular spectrum analysis is a highly effective method to denoise time series. MPA-ANN outperformed CPSOCGSA-ANN and SMA-ANN algorithms based on different statistical criteria. The suggested novel methodology offers good results with scatter index (SI) = 0.0009 and coefficient of determination (R2 = 0.98).http://dx.doi.org/10.1155/2022/6955271 |
| spellingShingle | Sarah J. Mohammed Salah L. Zubaidi Nadhir Al-Ansari Hussein Mohammed Ridha Nabeel Saleem Saad Al-Bdairi Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm Advances in Civil Engineering |
| title | Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm |
| title_full | Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm |
| title_fullStr | Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm |
| title_full_unstemmed | Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm |
| title_short | Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm |
| title_sort | hybrid technique to improve the river water level forecasting using artificial neural network based marine predators algorithm |
| url | http://dx.doi.org/10.1155/2022/6955271 |
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