Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model

Ehteram, Mohammad; El-Shafie, Amr; Hin, Lai Sai; Othman, Faridah; Koting, Suhana; Karami, Hojat; Mousavi, Sayed‐Farhad; Farzin, Saeed; Ahmed, Ali Najah; Zawawi, Mohd Hafiz; Hossain, Shabbir; Mohd, Nuruol Syuhadaa; Afan, Haitham Abdulmohsin; El‐Shafie, Ahmed

doi:10.3390/app9193960

Cited by 9 publications

(2 citation statements)

References 39 publications

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“…There are studies in which the SSO Algorithm is carried out in engineering fields such as dam construction, and construction of water reservoirs, especially to minimize the absolute difference between water demand and water release (Ehteram et al, 2017;Allawi et al, 2018;Ehteram et al, 2019;Cuevas et al, 2022).…”

Section: Shark Smell Optimization Algorithmmentioning

confidence: 99%

Artificial Intelligence Optimization Algorithms in the Finance Sector

YILDIRIM¹,

ONUR²,

KUNDURACI³

2023

uluabd

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Increasing efficiency and security expectations with the technological developments in the financial sector have necessitated more effective solutions in issues such as financial failure prediction, portfolio selection, market analysis, risk assessment, and stock price estimation. At this point, artificial intelligence optimization algorithms come to the fore in terms of lower cost, higher data storage capacity, and transaction volume, performing the same operations in similar problems and creating an effective order to prevent problems that may occur in the future. For this purpose, the use of artificial intelligence optimization algorithms in the finance sector is discussed in this study. To guide their use in decision-making and problem-solving processes, Artificial Bee Colony Algorithm, Tabu Search Algorithm, Flower Pollination Algorithm, Shark Smell Optimization Algorithm, and Differential Evolution Algorithm were explained and examples from their application areas were shared. As a result of the study, the advantages of artificial intelligence optimization algorithms were revealed and their importance in the finance sector was emphasized. This study is especially widely used in the solution of engineering problems; however, it contributes to the literature in terms of explaining algorithms whose applications in the finance sector are still limited.

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Section: Shark Smell Optimization Algorithmmentioning

confidence: 99%

Artificial Intelligence Optimization Algorithms in the Finance Sector

YILDIRIM¹,

ONUR²,

KUNDURACI³

2023

uluabd

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“…An important motivation for hydraulic engineers is whether a hybrid soft computing model with an evolutionary algorithm can improve the accuracy of the performance in a field where they have yet to be examined. Different optimization algorithms such as genetic algorithm (GA), firefly algorithm (FFA), shark algorithm (SA), and bat algorithm (BA) have been utilized to train the soft computing models [19][20][21][22][23]. In recent years, multilayer perceptron models have been applied to forecast the scour depth around hydraulic structures.…”

Section: Introductionmentioning

confidence: 99%

Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm

Ehteram

Ahmed

Ling

et al. 2020

Water

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In this research, the advanced multilayer perceptron (MLP) models are utilized to predict the free rate of expansion that usually occurs around the pipeline (PL) because of waves. The MLP model was structured by integrating it with three optimization algorithms: particle swarm optimization (PSO), whale algorithm (WA), and colliding bodies’ optimization (CBO). The sediment size, wave characteristics, and PL geometry were used as the inputs for the applied models. Moreover, the scour rate, vertical scour rate along the pipeline, and scour rate at both right and left sides of the pipeline were predicted as the model outputs. Results of the three suggested models, MLP-CBO, MLP-WA, and MLP-PSO, for both testing and training sessions were assessed based on different statistical indices. The results indicated that the MLP-CBO model performed better in comparison to the MLP-PSO, MLP-WA, regression, and empirical models. The MLP-CBO can be used as a powerful soft-computing model for predictions.

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