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Hybrid Technique to...
Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm
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- Mohammed, Sarah J. (författare)
- Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
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- Zubaidi, Salah L. (författare)
- Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
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- Al-Ansari, Nadhir, 1947- (författare)
- Luleå tekniska universitet,Geoteknologi
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- Ridha, Hussein Mohammed (författare)
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
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- Al-Bdairi, Nabeel Saleem Saad (författare)
- Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
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(creator_code:org_t)
- Hindawi Publishing Corporation, 2022
- 2022
- Engelska.
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Ingår i: Advances in Civil Engineering / Hindawi. - : Hindawi Publishing Corporation. - 1687-8086 .- 1687-8094. ; 2022
- Relaterad länk:
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https://doi.org/10.1...
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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).
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Vattenteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Water Engineering (hsv//eng)
Nyckelord
- Soil Mechanics
- Geoteknik
Publikations- och innehållstyp
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- art (ämneskategori)
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