Modelling groundwater level fluctuations in urban areas using artificial neural network

Malik, Ashish; Bhagwat, Anjali

doi:10.1016/j.gsd.2020.100484

Cited by 49 publications

(16 citation statements)

References 13 publications

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“…Poursaeid et al [24] tested different machine learning models to simulate the groundwater salinity from 15 years of times series data. Malik and Bhagwat [29] used different ANN architectures to estimate the GWL during the pre-and post-monsoon period at the Tikrit Kalan observation well located in Delhi, India. Elsayed et al [30] proposed the potential of the ANFIS model for estimation of GWL using the recorded data from the Kaharoa monitoring site in the North Island of New Zealand.…”

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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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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“…The AUC is a useful metric of sensitivity and specificity that may be used to examine a diagnostic test's intrinsic validity. AUC = 1 indicates that the diagnostic test is completely accurate in distinguishing between groundwater and non-groundwater [24]. This means that sensitivity and specificity are equal, and both false positive and false negative errors are zero.…”

Section: Validation Of the Modelsmentioning

confidence: 99%

“…Geographic information systems (GIS) and remote sensing have ushered in a new era in this area, allowing multi-parametric research [14,16,[21][22][23]. The choices of conditioning variables and the utilization of an efficient integration approach are crucial to effective modeling [10,[24][25][26][27][28]. Table 1 shows that some groundwater potentiality modelling conditioning factors, such as soil texture, groundwater level, annual rainfall, Normalized Difference Vegetation Index (NDVI), geology, land use land cover, elevation, slope, aspect, curvature, topographic wetness index (TWI), Terrain Ruggedness Index (TRI), stream power index (SPI), distance to river, and others, have been widely used [16,21,22,[29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 shows that some groundwater potentiality modelling conditioning factors, such as soil texture, groundwater level, annual rainfall, Normalized Difference Vegetation Index (NDVI), geology, land use land cover, elevation, slope, aspect, curvature, topographic wetness index (TWI), Terrain Ruggedness Index (TRI), stream power index (SPI), distance to river, and others, have been widely used [16,21,22,[29][30][31][32]. Elevation, slope, and rainfall have been identified as paramount parameters in the plains, while geology, lineament, and the other factors listed above have been identified as paramount variables in the mountainous area [24,[33][34][35][36]. It is worth noting that not all of the variables are necessary for all spatial units.…”

Section: Introductionmentioning

confidence: 99%

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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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“…However, the conversion of the physical processes into mathematical formulations, as well as the lack of sufficient data to execute the modelling process, is some of the challenges of using numerical models. Interestingly, artificial modelling techniques have been developed over the last two decades as an approach to overcome these shortcomings of the numerical models [7]- [9]. For instance, artificial neural network (ANN) model has been used for the prediction of fluctuations in groundwater level; the study relied on time-lagged water levels as the model input [10].…”

Section: Introductionmentioning

confidence: 99%

On the Evaluation of the Gradient Tree Boosting Model for Groundwater Level Forecasting

Naganna

Beyaztaş

Bokde

et al. 2020

Kbes

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Though groundwater is a replenishable resource, it’s over exploitation has posed greater problem of its depletion. Hence, monitoring and forecasting of groundwater levels has become a primary task of governmental water boards/agencies for sustainable water management. The current study focused on evaluating the performance of Gradient Tree Boosting (GTB) model with that of conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) and Group Method of Data Handling (GMDH) models in forecasting groundwater levels of two coastal aquifers. Data of two groundwater level monitoring wells penetrating into unconfined aquifers located at Shirtadi and Rayee near to Mangalore city of Karnataka state, India was considered in the present study. Monthly groundwater level data of the years 2000 – 2013 were used for model simulation; wherein 70% of data was used for model training and the remaining 30% served as testing data. Comparative result evaluation shows that the proposed GTB approach for one month ahead groundwater level forecasting was giving much accurate results than the other models for the same period of time and same set of data. For Rayee monitoring well, the error statistic, RRMSE of GTB, GMDH and ANFIS models obtained during test phase were 0.473, 0.517 and 0.7522, respectively. The comparison is examined further with different performance metrics.

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Modelling groundwater level fluctuations in urban areas using artificial neural network

Cited by 49 publications

References 13 publications

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

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

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

On the Evaluation of the Gradient Tree Boosting Model for Groundwater Level Forecasting

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