Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Water, Journal Year: 2023, Volume and Issue: 15(9), P. 1750 - 1750

Published: May 2, 2023

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

Language: Английский

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Novel evolutionary-optimized neural network for predicting landslide susceptibility

Environment Development and Sustainability, Journal Year: 2023, Volume and Issue: 26(7), P. 17687 - 17719

Published: May 19, 2023

Language: Английский

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A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility mapping

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(34), P. 82964 - 82989

Published: June 19, 2023

Language: Английский

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A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Jan. 7, 2024

This research aims to forecast, using various criteria, the flow of soil erosion that will occur at a particular geographical location. As for training dataset, 80% dataset from sample sites, four hybrid algorithms, namely heap-based optimizer (HBO), political (PO), teaching-learning based optimization (TLBO), and backtracking search algorithm (BSA) combined with artificial neural network (ANN) was used create an susceptibility model establishes unique original approach. After it confirmed be successful, algorithms were applied map this area, demonstrating integrity results. The AUC values computed every optimisation in study. optimal estimated accuracy indices populations 450 determined 0.9846 BSA-MLP databases. maximum value HBO-MLP databases different swarm sizes 0.9736. A size 350–300 is considered forecasting mapping models. With same constraints, TLBO-MLP scenario 0.996. 150 conditions train PO-MLP model, 0.9845. According these findings, worked best 50 150, respectively.

Language: Английский

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A new procedure for optimizing neural network using stochastic algorithms in predicting and assessing landslide risk in East Azerbaijan

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: March 21, 2024

Language: Английский

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Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(2), P. 1569 - 1589

Published: Jan. 12, 2024

Language: Английский

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Hybrid artificial intelligence models based on adaptive neuro fuzzy inference system and metaheuristic optimization algorithms for prediction of daily rainfall

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: 134, P. 103563 - 103563

Published: Jan. 28, 2024

Language: Английский

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Validation of four optimization evolutionary algorithms combined with artificial neural network (ANN) for landslide susceptibility mapping: A case study of Gilan, Iran

Ecological Engineering, Journal Year: 2024, Volume and Issue: 201, P. 107214 - 107214

Published: Feb. 29, 2024

Language: Английский

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Assessment of sodium adsorption ratio (SAR) in groundwater: Integrating experimental data with cutting-edge swarm intelligence approaches

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: April 29, 2024

Language: Английский

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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

Water, Journal Year: 2023, Volume and Issue: 15(13), P. 2453 - 2453

Published: July 4, 2023

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

Language: Английский

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