Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

Alizamir, Meysam; Kazemi, Zeinab; Kermani, Majid; Kim, Sungwon; Heddam, Salim; Kisi, Özgür; Chung, Il-Moon

doi:10.3390/w15132453

Cited by 4 publications

(1 citation statement)

References 50 publications

(75 reference statements)

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“…They utilize the backpropagation algorithm to adjust weights and reduce error. It comprises of three diverse layers, called input, hidden, and output layer [ 56 , 57 ]. In this method, each separate neuron must be linked to all following layer neurons, while the neurons should be arranged in a one-directional procedure, ultimately [ 45 , 58 ].…”

Section: Methodology and Model Developmentmentioning

confidence: 99%

Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms

Alizamir,

Ahmed,

Kim

et al. 2023

PLoS ONE

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Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly influenced by the ST. Additionally, ST indirectly affects plant growth by influencing the accessibility of nutrients in the soil. Therefore, designing an efficient tool for ST estimating at different depths is useful for soil studies by considering meteorological parameters as input parameters, maximal air temperature, minimal air temperature, maximal air relative humidity, minimal air relative humidity, precipitation, and wind speed. This investigation employed various statistical metrics to evaluate the efficacy of the implemented models. These metrics encompassed the correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe (NS) efficiency, and mean absolute error (MAE). Hence, this study presented several artificial intelligence-based models, MLPANN, SVR, RFR, and GPR for building robust predictive tools for daily scale ST estimation at 05, 10, 20, 30, 50, and 100cm soil depths. The suggested models are evaluated at two meteorological stations (i.e., Sulaimani and Dukan) located in Kurdistan region, Iraq. Based on assessment of outcomes of this study, the suggested models exhibited exceptional predictive capabilities and comparison of the results showed that among the proposed frameworks, GPR yielded the best results for 05, 10, 20, and 100cm soil depths, with RMSE values of 1.814°C, 1.652°C, 1.773°C, and 2.891°C, respectively. Also, for 50cm soil depth, MLPANN performed the best with an RMSE of 2.289°C at Sulaimani station using the RMSE during the validation phase. Furthermore, GPR produced the most superior outcomes for 10cm, 30cm, and 50cm soil depths, with RMSE values of 1.753°C, 2.270°C, and 2.631°C, respectively. In addition, for 05cm soil depth, SVR achieved the highest level of performance with an RMSE of 1.950°C at Dukan station. The results obtained in this research confirmed that the suggested models have the potential to be effectively used as daily predictive tools at different stations and various depths.

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Section: Methodology and Model Developmentmentioning

confidence: 99%

Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms

Alizamir,

Ahmed,

Kim

et al. 2023

PLoS ONE

Self Cite

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Electrical line fault prediction using a novel grey wolf optimization algorithm based on multilayer perceptron

Zhang

2024

Adv Control Appl

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Grey wolf optimization algorithm (GWO) has achieved great results in the optimization of neural network parameters. However, it has some problems such as insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal solution. Therefore, a grey wolf optimization algorithm combining Levy flight and nonlinear inertia weights (LGWO) is proposed in this paper. The combination of Levy flight and nonlinear inertia weight is to improve the search efficiency and solve the problem that the search ability is weak and it is easy to fall into the local optimal solution. In summary, LGWO solves the problems of insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal. This paper uses Congress on Evolutionary Computation benchmark function and combines algorithms with neural network for power line fault classification prediction to verify the effectiveness of each strategy improvement in LGWO and its comparison with other excellent algorithms (sine cosine algorithm, tree seed algorithm, wind driven optimization, and gravitational search algorithm). In the combination of neural networks and optimization algorithms, the accuracy of LGWO has been improved compared to the basic GWO, and LGWO has achieved the best performance in multiple algorithm comparisons.

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Assessing the impacts and contamination potentials of landfill leachate on adjacent groundwater systems

Huang,

Liu,

Zhang

et al. 2024

Science of The Total Environment

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Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

Cited by 4 publications

References 50 publications

Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms

Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms

Electrical line fault prediction using a novel grey wolf optimization algorithm based on multilayer perceptron

Assessing the impacts and contamination potentials of landfill leachate on adjacent groundwater systems

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