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Applicability of AN...
Applicability of ANN Model and CPSOCGSA Algorithm forMulti-Time Step Ahead River Streamflow Forecasting
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- Kareem, Baydaa Abdul (författare)
- Department of Civil Engineering, University of Maysan, Maysan 57000, Iraq; 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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- Ridha, Hussein Mohammed (författare)
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
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- Al-Ansari, Nadhir, 1947- (författare)
- Luleå tekniska universitet,Geoteknologi
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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)
- 2022-09-30
- 2022
- Engelska.
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Ingår i: Hydrology. - : MDPI. - 2306-5338. ; 9:10
- Relaterad länk:
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https://doi.org/10.3...
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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/10.3...
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Abstract
Ämnesord
Stäng
- Accurate streamflow prediction is significant when developing water resource management and planning, forecasting floods, and mitigating flood damage. This research developed a novel methodology that involves data pre-processing and an artificial neural network (ANN) optimised with the coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA-ANN) to forecast the monthly water streamflow. The monthly streamflow data of the Tigris River at Amarah City, Iraq, from 2010 to 2020, were used to build and evaluate the suggested methodology. The performance of CPSOCGSA was compared with the slim mold algorithm (SMA) and marine predator algorithm (MPA). The principal findings of this research are that data pre-processing effectively improves the data quality and determines the optimum predictor scenario. The hybrid CPSOCGSA-ANN outperformed both the SMA-ANN and MPA-ANN algorithms. The suggested methodology offered accurate results with a coefficient of determination of 0.91, and 100% of the data were scattered between the agreement limits of the Bland–Altman diagram. The research results represent a further step toward developing hybrid models in hydrology applications.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Oceanografi, hydrologi och vattenresurser (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Oceanography, Hydrology and Water Resources (hsv//eng)
Nyckelord
- streamflow prediction
- CPSOCGSA
- ANN
- metaheuristic algorithms
- SSA
- Soil Mechanics
- Geoteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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