Improving prediction of water quality indices using novel hybrid machine-learning algorithms

The Science of The Total Environment, Journal Year: 2020, Volume and Issue: 721, P. 137612 - 137612

Published: March 3, 2020

Language: Английский

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Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 29(25), P. 38346 - 38373

Published: Jan. 25, 2022

Language: Английский

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Evaluation of Groundwater Quality for Irrigation in Deep Aquifers Using Multiple Graphical and Indexing Approaches Supported with Machine Learning Models and GIS Techniques, Souf Valley, Algeria

Water, Journal Year: 2023, Volume and Issue: 15(1), P. 182 - 182

Published: Jan. 2, 2023

Irrigation has made a significant contribution to supporting the population’s expanding food demands, as well promoting economic growth in irrigated regions. The current investigation was carried out order estimate quality of groundwater for agricultural viability Algerian Desert using various water indices and geographic information systems (GIS). In addition, support vector machine regression (SVMR) applied forecast eight irrigation (IWQIs), such index (IWQI), sodium adsorption ratio (SAR), percentage (Na%), soluble (SSP), potential salinity (PS), Kelly (KI), permeability (PI), residual carbonate (RSC). Several physicochemical variables, temperature (T°), hydrogen ion concentration (pH), total dissolved solids (TDS), electrical conductivity (EC), K+, Na2+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, NO3−, were measured from 45 deep wells. hydrochemical facies resources Ca–Mg–Cl/SO4 Na–Cl−, which revealed evaporation, reverse exchange, rock–water interaction processes. IWQI, Na%, SAR, SSP, KI, PS, PI, RSC showed mean values 50.78, 43.07, 4.85, 41.78, 0.74, 29.60, 45.65, −20.44, respectively. For instance, IWQI obtained results indicated that samples categorized into high restriction moderate purposes, can only be used plants are highly salt tolerant. SVMR model produced robust estimates IWQIs calibration (Cal.), with R2 varying between 0.90 0.97. Furthermore, validation (Val.), 0.88 0.95 achieved model, reliable IWQIs. These findings feasibility models evaluation management complex terminal aquifers irrigation. Finally, combination IWQIs, SVMR, GIS effective an applicable technique interpreting forecasting both arid semi-arid

Language: Английский

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A multi-model study for understanding the contamination mechanisms, toxicity and health risks of hardness, sulfate, and nitrate in natural water resources

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(22), P. 61626 - 61658

Published: March 17, 2023

Language: Английский

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Towards sustainable industrial development: modelling the quality, scaling potential and corrosivity of groundwater using GIS, spatial statistics, soft computing and index-based methods

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: June 21, 2024

Language: Английский

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River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques

Procedia Computer Science, Journal Year: 2017, Volume and Issue: 120, P. 75 - 82

Published: Jan. 1, 2017

In this study, multi linear regression ( MLR), artificial neural network (ANN) and adaptive neuro fuzzy inference system(ANFIS) techniques were developed to predict the Dissolve oxygen concentration at down stream of Agra city, using monthly input data which are dissolve oxygen(DO), pH, biological demand(BOD) water temperature (WT) three different places viz, upstream, middle downstream. Initially, 11 parameters for all locations used except DO downstream, then, 7 downstream target location finally was considered in analysis. The performance evaluated determination coefficient (DC) root mean square error (RMSE), result showed that both ANN ANFIS can be applied modelling also indicate that, model is slightly better than indicates a considerable superiority MLR.

Language: Английский

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Determining quality of water in reservoir using machine learning

Ecological Informatics, Journal Year: 2018, Volume and Issue: 44, P. 57 - 75

Published: Feb. 1, 2018

Language: Английский

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Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation

Water Resources Management, Journal Year: 2018, Volume and Issue: 32(7), P. 2227 - 2245

Published: Feb. 8, 2018

Language: Английский

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Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination

Journal of Hydrology, Journal Year: 2020, Volume and Issue: 587, P. 124974 - 124974

Published: April 18, 2020

Language: Английский

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Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index

Environmental Science and Pollution Research, Journal Year: 2020, Volume and Issue: 27(33), P. 41524 - 41539

Published: July 20, 2020

Language: Английский

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