Forecasting hydropower generation by<scp>GFDL‐CM3</scp>climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (<scp>ISSA</scp>)

Wang, Haizhen; Wu, Xuerui; Gholinia, Fatemeh

doi:10.1002/cpe.6476

Cited by 18 publications

(6 citation statements)

References 59 publications

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“…This model is designed to evaluate the effects of management activities on the movement of water and sediment and water resources in nonstation watersheds. This model is a physical model, and instead of applying regression equations to explain the correlation between input and output factors, it collects special information on air, soil, topography, land‐cover, and land use in the watersheds 25 . The benefits of this method are that firstly, areas that lack data such as flow information are simulated.…”

Section: Methodsmentioning

confidence: 99%

“…This model is a physical model, and instead of applying regression equations to explain the correlation between input and output factors, it collects special information on air, soil, topography, land-cover, and land use in the watersheds. 25 The benefits of this method are that firstly, areas that lack data such as flow information are simulated. And secondly, the impact of input information (changes in management methods, climate, and land-cover) on water quality and other factors can be measured.…”

Section: Introduction Soil and Water Assessment Tools (Swat) Modelmentioning

confidence: 99%

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Using an optimized soil and water assessment tool by deep belief networks to evaluate the impact of land use and climate change on water resources

Khayatnezhad

Youssefi

2022

Concurrency and Computation

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This article investigates the negative effect of land use and climate changes on water resources by the SWAT and SWAT-DEEP\LMSFO model. Due to the importance of runoff impact on water resources in this article, the hybrid hydrological-deep neural networks optimized by the improved SFO based on logistic map (LMSFO) algorithm have been used to provide more accurate results for runoff estimation. This method improves runoff simulation. Firstly, runoff under the influence of land use and climate change is estimated by the SWAT model. Once again, runoff is estimated by the SWAT-DEEP/LMSFO model. In the DEEP/LMSFO model, the primary runoff has been estimated by an un-calibrated SWAT model. Then the Primary runoff simulated is entered as an input into the DEEP model. Finally, the runoff is estimated by a test-training method. The results of the SWAT model and the DEEP/LMSFO model show that there is an inverse correlation between land use so that reducing land cover increases runoff. The results show that climate change and land-use change can affect annual runoff changes in the coming years.

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Section: Methodsmentioning

confidence: 99%

Section: Introduction Soil and Water Assessment Tools (Swat) Modelmentioning

confidence: 99%

Using an optimized soil and water assessment tool by deep belief networks to evaluate the impact of land use and climate change on water resources

Khayatnezhad

Youssefi

2022

Concurrency and Computation

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“…A hydrological-neural network hybrid system for flow prediction was proposed by Wang et al [ 96 ] using an Improved SSA algorithm. Using such a model showed that there will be a decrease in the average annual electricity that is generated by hydropower under many scenarios.…”

Section: Recent Variants Of Ssamentioning

confidence: 99%

“…The environment is another vital area that is tackled by SSA with the following solutions proposed processing of coal mine water source data [ 100 ], recognition of a linear source contamination [ 20 ], comprehensive water quality evaluation [ 41 , 46 ], atmospheric prediction [ 65 ], short-term multi-step wind speed forecasting [ 36 ], forecasting hydropower generation by GFDL-CM3 climate model [ 96 ], and short-term wind power forecasting [ 47 ].…”

Section: Applications Of Ssamentioning

confidence: 99%

Recent Versions and Applications of Sparrow Search Algorithm

Awadallah

Al-Betar

Doush

et al. 2023

Arch Computat Methods Eng

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This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized for a plethora of optimization problems in different research topics, such as mechanical engineering, electrical engineering, civil engineering, power systems, industrial engineering, image processing, networking, environment, robotics, planing and scheduling, and healthcare. Initially, the growth of SSA and its theoretical features are highlighted in terms of the number of published articles, citations, topics covered, etc. After that, the different extended versions of SSA are reviewed, where the main variations of SSA are produced to avoid premature convergence and to boost the diversity aspects. These extended versions are modifications and hybridization summarized with more focus on the motivations behind establishing these versions. Multi-objective SSA is also presented as another version to deal with Multi-objective optimization problems. The critical analysis of the main research gaps in the convergence behaviour of SSA is discussed. Finally, the conclusion and the possible future expansions are recommended based on the research works accomplished in the literature.

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“…Zhang et al [31] proposed an improved sparrow search algorithm with three new strategies for a bioinspired path planning approach for mobile robots. Wang et al [32] proposed an improved sparrow search algorithm for a hydrological neural network hybrid model. Liang et al [33] proposed a new intelligent optimization algorithm called the sparrow search algorithm (SSA) and its modification for the electromagnetics and antenna community.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation

Yang

Liu

et al. 2021

Applied Sciences

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Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow search algorithm, namely the CWTSSA in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. An adaptive t-distribution mutation operator is designed, which uses the iteration number t as the degree of freedom parameter of the t-distribution to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. In order to prove the effectiveness of the CWTSSA, 15 standard test functions and other improved SSAs, differential evolution (DE), particle swarm optimization (PSO), gray wolf optimization (GWO) are selected here. The compared experiment results indicate that the proposed CWTSSA can obtain higher convergence accuracy, faster convergence speed, better diversity and exploration abilities. It provides a new optimization algorithm for solving complex optimization problems.

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Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Cited by 18 publications

References 59 publications

Using an optimized soil and water assessment tool by deep belief networks to evaluate the impact of land use and climate change on water resources

Using an optimized soil and water assessment tool by deep belief networks to evaluate the impact of land use and climate change on water resources

Recent Versions and Applications of Sparrow Search Algorithm

A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation

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