Assessment of artificial neural network models based on the simulation of groundwater contaminant transport

Pal, Jayashree; Chakrabarty, Dibakar

doi:10.1007/s10040-020-02180-4

Cited by 17 publications

(10 citation statements)

References 49 publications

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“… 25 predicted trihalomethanes levels in tap water using RBF and gray relational analysis. 26 have evaluated GR neural network models in simulating the groundwater contaminant transport. 27 have implemented the supervised intelligence committee machine method to predict the reservoir water level variation for the design and operation of dams.…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Intelligence Techniques for the Determination of Groundwater Level Using Spatio–Temporal Parameters

2022

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Increasing the depth of mining leads to the location of the mine pit below the groundwater level. The entry of groundwater into the mining pit increases costs as well as reduces efficiency and the level of work safety. Prediction of the groundwater level is a useful tool for managing groundwater resources in the mining area. In this study, to predict the groundwater level, multilayer perceptron, cascade forward, radial basis function, and generalized regression neural network models were developed. Moreover, four optimization algorithms, including Bayesian regularization, Levenberg–Marquardt, resilient backpropagation, and scaled conjugate gradient, are used to improve the performance and prediction ability of the multilayer perception and cascade forward neural networks. More than 1377 data points including 12 spatial parameters divided into two categories of sediments and bedrock (longitude, latitude, hydraulic conductivity of sediments and bedrock, effective porosity of sediments and bedrock, the electrical resistivity of sediments and bedrock, depth of sediments, surface level, bedrock level, and fault), and besides, 6 temporal parameters are used (day, month, year, drainage, evaporation, and rainfall). Also, to determine the best models and combine them, 165 extra validation data points are used. After identifying the best models from the three candidate models with a lower average absolute relative error (AARE) value, the committee machine intelligence system (CMIS) model has been developed. The proposed CMIS model predicts groundwater level data with high accuracy with an AARE value of less than 0.11%. Sensitivity analysis indicates that the electrical resistivity of sediments had the highest effect on the groundwater level. Outliers’ estimation applying the Leverage approach suggested that only 2% of the data points could be doubtful. Eventually, the results of modeling and estimating groundwater level fluctuations with low error indicate the high accuracy of machine learning methods that can be a good alternative to numerical modeling methods such as MODFLOW.

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Section: Introductionmentioning

confidence: 99%

“…modeled river flow using optimized CF and MLP in the Kelantan River in Malaysia . predicted trihalomethanes levels in tap water using RBF and gray relational analysis . have evaluated GR neural network models in simulating the groundwater contaminant transport .…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Techniques for the Determination of Groundwater Level Using Spatio–Temporal Parameters

2022

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“…Different methods of AI, including arti cial neural networks (ANNs), fuzzy-neural networks, self-organized maps (SOM), and machine learning (ML), have been applied extensively in the groundwater modeling process. AI-based models have been used in groundwater quality studies (Chou 2006;Chou 2007;Han et al 2011;Wang et al 2014;Li et al 2020;Maliqi et al 2020;Pal and Chakrabarty 2020;Mosaffa et al 2021), groundwater depth studies (Dixon 2004;Awasthi et al 2005;Saemi and Ahmadi 2008;Shiri et al 2013;Gong et al 2018;Chen et al 2020), and the other hydrological studies (Smith and Eli 1995;Dawson 1998;Cheng et al 2002;Chau and Cheng 2002;Mehr et al 2003;Wilby et al 2003;Jain et al 2004;Anctil and Rat 2005;Cheng et al 2005;Peters et al 2006;Demirel et al 2009;Nourani et al 2011;Can et al 2012;Demirel et al 2012;Kisi et al 2013;Nourani et al 2013;Kisi 2015;Taormina and Chau 2015;Nourani et al 2017Nourani et al & 2018Nourani et al 2019 a&b). Ghose et al (2010) used ANN for simulating water table in the region of Orissa and they found that the ANN is very e cient in water table simulation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of groundwater drawdown using artificial neural networks

Gholami

Sahour

2022

Environ Sci Pollut Res

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Groundwater drawdown is typically measured using pumping tests and eld experiments; however, the traditional methods are time-consuming and costly when applied to extensive areas. In this research, a methodology is introduced based on arti cial neural network (ANN)s and eld measurements in an alluvial aquifer in the north of Iran. First, the annual drawdown as the output of the ANN models in 250 piezometric wells was measured, and the data were divided into three categories of training data, cross-validation data, and test data. Then, the effective factors in groundwater drawdown including groundwater depth, annual precipitation, annual evaporation, the transmissivity of the aquifer formation, elevation, distance from the sea, distance from water sources (recharge), population density, and groundwater extraction in the in uence radius of each well (1000 m) were identi ed and used as the inputs of the ANN models. Several ANN methods were evaluated, and the predictions were compared with the observations. Results show that, the modular neural network (MNN) showed the highest performance in modeling groundwater drawdown (Training R-sqr = 0.96, test R-sqr = 0.81). The optimum network was tted to available input data to map the annual drawdown across the entire aquifer. The accuracy assessment of the nal map yielded favorable results (R-sqr = 0.8). The adopted methodology can be applied for the prediction of groundwater drawdown in the study site and similar settings elsewhere.

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“…More recent methods such as artificial intelligent (AI) models perform well under limited data situations and are able to predict pollutant concentrations in groundwater systems. Examples are artificial neural network (ANN), Fuzzy Logic, and multi-criteria decision-making which have been applied by researchers worldwide to assess the groundwater vulnerability (Kisi et al, 2019;Mallik et al, 2020;Maroufpoor et al, 2019Maroufpoor et al, , 2020Mohamed et al, 2019;Ostad-Ali-Askari et al, 2017;Pal & Chakrabarty, 2020;Sunayana et al, 2020;Vadiati et al, 2016;Wagh et al, ,,2016Wagh et al, ,, , 2017.…”

Section: Introductionmentioning

confidence: 99%

Simulating contaminant transport in unsaturated and saturated groundwater zones

Sarma

Singh

2021

Water Environment Research

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The demand for clean and adequate water is rising rapidly with increasing population. This growing demand for water necessitates the measurement of the quantity and quality of water. Simulation modeling has become increasingly popular in the last two decades largely because of their predictive ability. This paper reviews the approaches for simulation modeling in groundwater resources management, focusing on models that have been used to simulate contaminant transport through the aquifer system. Recent research papers that have integrated the models for unsaturated and saturated zones have also been studied and described. Integrated models require assessment of the complex interactions between the groundwater zones and the movement of water and solute through them. Due to this, integrated models provide a more accurate modeling approach than models that have been independently developed for saturated and unsaturated zones. Application of such models is encouraged at the regional level to arrive at the best groundwater management decisions. © 2021 Water Environment Federation • Practitioner points• In the past few decades, modeling of contaminant transport in groundwater systems has seen tremendous applications. • A number of models exist that independently simulate flow and solute transport in unsaturated and saturated zones. • Recently, focus has been given on developing advanced coupled modeling approaches that require less inputs and run times.

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Assessment of artificial neural network models based on the simulation of groundwater contaminant transport

Cited by 17 publications

References 49 publications

Application of Artificial Intelligence Techniques for the Determination of Groundwater Level Using Spatio–Temporal Parameters

Application of Artificial Intelligence Techniques for the Determination of Groundwater Level Using Spatio–Temporal Parameters

Prediction of groundwater drawdown using artificial neural networks

Simulating contaminant transport in unsaturated and saturated groundwater zones

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