Analysis of groundwater quality for drinking purposes using combined artificial neural networks and fuzzy logic approaches

Güner, Esra Deniz; Kuvvetli, Yusuf

doi:10.5004/dwt.2020.24839

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Among the data-driven methods, machine learning has recently shown promising results in the advancement of accurate models. The artificial neural networks (ANN) [23][24][25][26][27][28], support vector machines (SVM) [29][30][31][32], decision trees (DT) [33], adaptive network-based fuzzy inference system (ANFIS) [34][35][36], extreme learning machines (ELM) [37], multilayer perceptron (MLP) [38], K-nearest neighbors (KNN) [39], wavelet neural networks (WNN) [28], and supervised intelligence committee machine (SICK) [40] are among the notable machine learning methods used for advancing susceptibility mapping for groundwater quality.…”

Section: Introductionmentioning

confidence: 99%

Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

Mosavi

Hosseini

Choubin

et al. 2020

Water

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Groundwater resources, unlike surface water, are more vulnerable to disturbances and contaminations, as they take a very long time and significant cost to recover. So, predictive modeling and prevention strategies can empower policymakers for efficient groundwater governance through informed decisions and recommendations. Due to the importance of groundwater quality modeling, the hardness susceptibility mapping using machine learning (ML) models has not been explored. For the first time, the current research aimed to predict groundwater hardness susceptibility using the ML models. The performance of two ensemble models of boosted regression trees (BRT) and random forest (RF) is investigated through the arrangement of a comparative study with multivariate discriminant analysis (MDA). According to the hardness values in 135 groundwater quality monitoring wells, the hard and soft water are determined; then, 11 predictor variables including distance from the sea (DFS), land use, elevation, distance from the river (DFR), depth to groundwater (DTGW), pH, precipitation (PCP), evaporation (E), groundwater level (GWL), curvature, and lithology are used for predicting the groundwater hardness susceptibility map. Results indicated that the variables of DFR, DTGW, elevation, and DFS had a higher contribution to the modeling process. So, the high harness areas are mostly related to low elevations, low DTGW, and proximity to river and sea, which facilitate the percolation conditions for minerals containing calcium or magnesium into groundwater.

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Section: Introductionmentioning

confidence: 99%

Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

Mosavi

Hosseini

Choubin

et al. 2020

Water

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The combination of fuzzy analytical hierarchical process and maximum entropy methods for the selection of wind farm location

ÇİLEK

Güner

Tekin

2022

Environ Sci Pollut Res

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Research into the Optimal Regulation of the Groundwater Table and Quality in the Southern Plain of Beijing Using Geographic Information Systems Data and Machine Learning Algorithms

Men

Yin

et al. 2022

IJGI

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The purpose of this paper is to provide new ideas and methods for the sustainable use of groundwater in areas with serious groundwater overexploitation and serious groundwater pollution. Geographic information systems (GIS) were combined with machine learning algorithms, water resources optimization technology, and groundwater numerical simulation to optimize the regulation of the groundwater table and quality beneath the Daxing District in the southern plain of Beijing. By collecting local consumption and supply data and observations of the groundwater table and quality in the connected aquifer beneath Daxing for the years 2006–2020, the corresponding water demands and groundwater impact were extrapolated for the years 2021–2025 based on the basis of the existing development model. Through the combination of GIS and machine learning algorithms, the NO3-N concentration of local groundwater monitoring points in wet years, normal years, and dry years were predicted. With respect to NO3-N pollution, three new groundwater exploitation regimes were devised, which we numbered 1 to 3. The optimal allocation of water resources was then calculated for wet year, typical year, and dry year scenarios for the year 2025. By comparing the water shortage, groundwater utilization rate, and NO3-N pollution under the new groundwater exploitation regimes, the optimal groundwater exploitation mode for the three different types of hydrological year was determined. The results indicate that NO3-N pollution was greatly reduced after the adoption of the optimal regimes and that the groundwater table demonstrated rapid recovery. These results can be of great help in realizing the management, supervision, and regulation of groundwater by combining GIS with machine learning algorithms.

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Analysis of groundwater quality for drinking purposes using combined artificial neural networks and fuzzy logic approaches

Cited by 3 publications

References 19 publications

Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

The combination of fuzzy analytical hierarchical process and maximum entropy methods for the selection of wind farm location

Research into the Optimal Regulation of the Groundwater Table and Quality in the Southern Plain of Beijing Using Geographic Information Systems Data and Machine Learning Algorithms

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