Mohammed, SJ, Zubaidi, SL, Al-Ansari, N, Mohammed Ridha, H, Dulaimi, A and Al-Khafaji, R (2023) Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting. Advances in Civil Engineering, 2023. pp. 1-15. ISSN 1687-8086

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Abstract

With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors' scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts.

Item Type:	Article
Uncontrolled Keywords:	0905 Civil Engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Civil Engineering & Built Environment
Publisher:	Hindawi Limited
SWORD Depositor:	A Symplectic
Date Deposited:	04 Jul 2023 10:57
Last Modified:	04 Jul 2023 11:00
DOI or ID number:	10.1155/2023/9947603
Editors:	Yaseen, ZM
URI:	https://researchonline.ljmu.ac.uk/id/eprint/20212

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