Groundwater quality evaluation using hybrid model of the multi-layer perceptron combined with neural-evolutionary regression techniques: case study of Shiraz plain

Moayedi, Hossein; Salari, Marjan; Dehrashid, Atefeh Ahmadi; Le, Binh Nguyen

doi:10.1007/s00477-023-02429-w

Cited by 21 publications

(7 citation statements)

References 82 publications

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“…They showed that ANFIS-PSO had the best accuracy for predicting groundwater salinization compared to ANFIS-GA and ANFIS-BBO [53]. Moayedi et al (2023) predicted groundwater quality parameters employing two-stage ML models combining ANN and three metaheuristic optimization algorithms (i.e., artificial bee colony (ABC), Harris hawks optimization (HHO), and GWO), Iran. This research illustrated that ANN-GWO provided the best prediction compared to ANN-HHO and ANN-ABC [28].…”

Section: Discussionmentioning

confidence: 99%

“…By applying the GWO algorithm, the accuracy of the SVM model was improved by 8.6% [27]. Moayedi et al (2023) assessed the precision of three ML paradigms, namely grey wolf optimization (GWO), artificial bee colony (ABC), and Harris hawks Optimization (HHO) intelligence models, in predicting the total hardness of groundwater quality in the Shiraz Plain, Iran. The results demonstrated that the GWO-ANN approach exhibited a high accuracy and capability in simulating and evaluating the quality of groundwater [28].…”

Section: Introductionmentioning

confidence: 99%

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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

Alizamir

Kazemi

Kermani

et al. 2023

Water

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The likelihood of surface water and groundwater contamination is higher in regions close to landfills due to the possibility of leachate percolation, which is a potential source of pollution. Therefore, proposing a reliable framework for monitoring leachate and groundwater parameters is an essential task for the managers and authorities of water quality control. For this purpose, an efficient hybrid artificial intelligence model based on grey wolf metaheuristic optimization algorithm and extreme learning machine (ELM-GWO) is used for predicting landfill leachate quality (COD and BOD5) and groundwater quality (turbidity and EC) at the Saravan landfill, Rasht, Iran. In this study, leachate and groundwater samples were collected from the Saravan landfill and monitoring wells. Moreover, the concentration of different physico-chemical parameters and heavy metal concentration in leachate (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, Ca, Na, NO3, Cl, K, COD, and BOD5) and in groundwater (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, EC, TDS, pH, Cl, Na, NO3, and K). The results obtained from ELM-GWO were compared with four different artificial intelligence models: multivariate adaptive regression splines (MARS), extreme learning machine (ELM), multilayer perceptron artificial neural network (MLPANN), and multilayer perceptron artificial neural network integrated with grey wolf metaheuristic optimization algorithm (MLPANN-GWO). The results of this study confirm that ELM-GWO considerably enhanced the predictive performance of the MLPANN-GWO, ELM, MLPANN, and MARS models in terms of the root-mean-square error, respectively, by 43.07%, 73.88%, 74.5%, and 88.55% for COD; 23.91%, 59.31%, 62.85%, and 77.71% for BOD5; 14.08%, 47.86%, 53.43%, and 57.04% for turbidity; and 38.57%, 59.64%, 67.94%, and 74.76% for EC. Therefore, ELM-GWO can be applied as a robust approach for investigating leachate and groundwater quality parameters in different landfill sites.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Alizamir

Kazemi

Kermani

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…The elevation of an area, which refers to its height above sea level, plays a crucial role in influencing various aspects related to groundwater, such as pressure, flow direction, and hydraulic gradient (Bien et al., 2023; Dai, Ju, et al., 2024; Moayedi et al., 2023). Altitude difference represents how much variation there is in height within an area and can affect how water flows over it and infiltrates into the ground.…”

Section: Methodsmentioning

confidence: 99%

Novel Ensemble Models Based on the Split‐Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping

Wang,

Le,

Tian

et al. 2024

Earth and Space Science

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Groundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater.

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“…One approach that has gained increasing attention in recent years is the use of hybrid AI models, which combine different AI techniques with traditional models or expert knowledge to improve performance and address some of the limitations of AI in groundwater management [25][26][27]. For example, a hybrid AI model might combine a neural network for predicting groundwater levels with a physically-based model for simulating flow and transport processes [28,29].…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Zaresefat

Derakhshani

2023

Water

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Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial for improving water resources planning and management. In the past 20 years, significant progress has been made in groundwater management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances in this field, existing literature must cover groundwater management using hybrid ML. This review article aims to understand the current state-of-the-art hybrid ML models used for groundwater management and the achievements made in this domain. It includes the most cited hybrid ML models employed for groundwater management from 2009 to 2022. It summarises the reviewed papers, highlighting their strengths and weaknesses, the performance criteria employed, and the most highly cited models identified. It is worth noting that the accuracy was significantly enhanced, resulting in a substantial improvement and demonstrating a robust outcome. Additionally, this article outlines recommendations for future research directions to enhance the accuracy of groundwater management, including prediction models and enhance related knowledge.

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Groundwater quality evaluation using hybrid model of the multi-layer perceptron combined with neural-evolutionary regression techniques: case study of Shiraz plain

Cited by 21 publications

References 82 publications

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

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

Novel Ensemble Models Based on the Split‐Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

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