Comprehensive water quality evaluation based on kernel extreme learning machine optimized with the sparrow search algorithm in Luoyang River Basin, China

Song, Chenguang; Yao, Lei; Hua, Chengya; Ni, Qihang

doi:10.1007/s12665-021-09879-x

Cited by 28 publications

(13 citation statements)

References 34 publications

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“…Also, DWT helps extract hidden information from input data by providing frequency and time information simultaneously. The superiority of hybrid algorithms compared to classical algorithms and the improvement of the accuracy of simulation algorithms by optimization algorithms have been shown in other studies (Azad et al 2018;Milan et al 2021;Song et al 2021aSong et al , 2021bWu et al 2022). Also, the simultaneous effect of DWT and optimization algorithms on increasing the accuracy of simulation algorithms has been proven by Montaseri et al (2018) and Anaraki et al (2021).…”

Section: Discussionmentioning

confidence: 75%

“…The results showed that the LSSVM-GBO performs better than classical algorithms. Song et al (2021aSong et al ( , 2021b developed a new hybrid algorithm called synchrosqueezed wavelet transform (WT)improved sparrow search algorithm (ISSA)-LSTM to predict water quality. The results showed that the new hybrid algorithm has high accuracy.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

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A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

Kadkhodazadeh

Farzin

2022

Journal of Water and Climate Change

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Improving the performance of machine learning (ML) algorithms is essential for accurately estimating water quality parameters (WQPs). For the first time, a novel hybrid framework, namely the adaptive neural fuzzy inference system–discrete wavelet transform–gradient-based optimization (ANFIS–DWT–GBO), for estimation of electrical conductivity (EC) and total dissolved solids (TDS) is used. Before estimating WQPs, the performance of the ANFIS–DWT–GBO is proven by several benchmark data sets. In addition, three benchmark algorithms, including ANFIS, ANFIS–DWT, and ANFIS–GBO, are used to demonstrate the strength of the novel framework. The principal component analysis (PCA) method determines the best input combination in EC and TDS estimation. The consequences show that the ANFIS–DWT–GBO produces very successful and competitive results in benchmark data sets modeling and WQPs estimation compared to other algorithms. This result is due to the simultaneous use of DWT and optimization algorithm in the proposed framework. DWT can process WQP data before applying it to the algorithms. The GBO is utilized to optimize the hyperfine parameters in the ANFIS. The results show that the highest accuracy of estimating EC and TDS is in Mollasani and Gotvand stations, respectively. The correlation coefficient (R) value in the Mollasani station is 0.99, and in the Gotvand station is 0.98.

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Section: Discussionmentioning

confidence: 75%

Section: Graphical Abstract Introductionmentioning

confidence: 99%

A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

Kadkhodazadeh

Farzin

2022

Journal of Water and Climate Change

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“…Song et al [ 46 ] addressed the water environmental quality management problem by proposing a new model. The kernel extreme learning machine was used to enhance the proposed model, and the SSA was utilized to optimize the model’s parameters.…”

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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“…In the formula, X best is the current global optimal position; β is the random number of the step size control parameter; K ∈ [−1,1] is a random number; f i is the fitness value of the current sparrow individual; f g is the current global optimal fitness value; f w is the current global worst fitness value; ε is a constant [ 27 ].…”

Section: Correlation Model Theorymentioning

confidence: 99%

Research on Blended Teaching of Flipped Classroom Based on CNN-SSA-Bi-LSTM Deep Learning Model Computer Media

Lü¹

2022

Computational Intelligence and Neuroscience

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Aiming at the problem that the influencing factors of computer media flipped classroom hybrid teaching lead to the teaching effect not reaching the expected, this study proposes an ultra-short-term prediction model based on CNN-SSA-Bi-LSTM. CNN-SSA-Bi-LSTM is used to flip the study of mixed teaching in the classroom. This method constructs a one-dimensional convolutional neural network, performs data fusion and feature transformation on multiple key variables, and then constructs a two-way long-term short-term memory network prediction model, which realizes a 45-minute classroom for ultra-short-term prediction of the future. In addition, data optimization is performed through SSA to improve the predictive effect of the CNN-Bi-LSTM model. Experimental results show that compared with the traditional machine learning method, the proposed prediction model can effectively improve the prediction accuracy of the ultra-short-term classroom effect, and the relative variance of the continuous model is increased by 16.22%. High prediction accuracy and low error prove that CNN-SSA-Bi-LSTM deep learning model has strong application prospects in the research of flipped classroom hybrid teaching.

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Comprehensive water quality evaluation based on kernel extreme learning machine optimized with the sparrow search algorithm in Luoyang River Basin, China

Cited by 28 publications

References 34 publications

A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

Recent Versions and Applications of Sparrow Search Algorithm

Research on Blended Teaching of Flipped Classroom Based on CNN-SSA-Bi-LSTM Deep Learning Model Computer Media

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