Susceptibility mapping of groundwater salinity using machine learning models

Mosavi, Amir; Hosseini, Farzaneh Sajedi; Choubin, Bahram; Taromideh, Fereshteh; Ghodsi, Maryam; Nazari, Bijan; Dineva, Adrienn

doi:10.1007/s11356-020-11319-5

Cited by 66 publications

(13 citation statements)

References 89 publications

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“…Based on these findings, the paper recommends utilising hybrid models and ensemble models for multi-parametric spatial prediction. To the authors' knowledge, although there has been no previous study that used these models in this study area, it should be noted that the LR-based hybrid model has demonstrated good performance in other environmental fields such as livelihood risk prediction [115,116], groundwater salinity [117], stream-flow prediction [118], piping erosion [119], and flashflood hazard assessment [120,121]. However, it can be noted that state-of-the-art machine learning models outperform older approaches in most cases [121].…”

Section: Discussionmentioning

confidence: 98%

A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

Mallick

Talukdar

Kahla

et al. 2021

Water

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The present work aims to build a unique hybrid model by combining six fuzzy operator feature selection-based techniques with logistic regression (LR) for producing groundwater potential models (GPMs) utilising high resolution DEM-derived parameters in Saudi Arabia’s Bisha area. The current work focuses exclusively on the influence of DEM-derived parameters on GPMs modelling, without considering other variables. AND, OR, GAMMA 0.75, GAMMA 0.8, GAMMA 0.85, and GAMMA 0.9 are six hybrid models based on fuzzy feature selection. The GPMs were validated by using empirical and binormal receiver operating characteristic curves (ROC). An RF-based sensitivity analysis was performed in order to examine the influence of GPM settings. Six hybrid algorithms and one unique hybrid model have predicted 1835–2149 km2 as very high and 3235–4585 km2 as high groundwater potential regions. The AND model (ROCe-AUC: 0.81; ROCb-AUC: 0.804) outperformed the other models based on ROC’s area under curve (AUC). A novel hybrid model was constructed by combining six GPMs (considering as variables) with the LR model. The AUC of ROCe and ROCb revealed that the novel hybrid model outperformed existing fuzzy-based GPMs (ROCe: 0.866; ROCb: 0.892). With DEM-derived parameters, the present work will help to improve the effectiveness of GPMs for developing sustainable groundwater management plans.

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Section: Discussionmentioning

confidence: 98%

A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

Mallick

Talukdar

Kahla

et al. 2021

Water

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“…The ANN model has been used to measure the groundwater salinity pattern in island aquifers [27]. Mosavi et al [28] produced Susceptibility mapping of groundwater salinity by using several machine learning models. Poursaeid et al [24] tested different machine learning models to simulate the groundwater salinity from 15 years of times series data.…”

Section: Literature Reviewmentioning

confidence: 99%

Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

Benaafi

Yassin

Usman³

et al. 2022

Sustainability

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Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca2+, Cl−, Br−, NO3−, and Fe, and Na+, SO42−, Mg2+, and F− combinations. HW-M1 justified promising among all the models with MBE = 1.41 × 10−11, 1.14 × 10−14, and MSE = 7.52 × 10−2, 3.88 × 10−11 for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks.

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“…ML techniques are growing in importance every day because of the ability to independently adapt to new data and learn from its previous calculations to produce reliable and even accurate predictions (Alizamir et al, 2020). For example, different ML models proved to be reliable in mapping groundwater salinity (Mosavi et al, 2021b) and used for groundwater hardness susceptibility mapping (Mosavi et al, 2020). Huang and Tian (2015) applied and made a comparison on the usage of three different models, which were an artificial neural network (ANN), a support vector machine (SVM), and an M5 T, to predict GWLs in China.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

Afan

Osman

Essam

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells. The results from S1 prove that the ensemble EDL generally performs superior to the DL in the estimation of GWL of each station using data of remaining four wells except the Paya Indah Wetland in which the DL method provide better estimates compared to EDL. Regarding S2, the EDL also exhibits superior performance in predicting daily GWL in all five stations compared to the DL model. Implementing EDL decreased the RMSE, NAE and RRMSE by 11.6%, 27.3% and 22.3% and increased the R, Spearman rho and Kendall tau by 0.4%, 1.1% and 3.5%, respectively. Moreover, EDL for S2 shows a high level of precision within less time lag, ranging between 2 and 4 compared to DL. Therefore, the EDL model has the potential in managing the sustainability of groundwater in Malaysia.

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Susceptibility mapping of groundwater salinity using machine learning models

Cited by 66 publications

References 89 publications

A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

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