Modeling and mapping of groundwater potentiality zones using AHP and GIS technique: a case study of Raniganj Block, Paschim Bardhaman, West Bengal

Chakrabortty, Rabin; Pal, Subodh Chandra; Malik, Sadhan; Das, Biswajit

doi:10.1007/s40808-018-0471-8

Cited by 81 publications

(27 citation statements)

References 71 publications

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“…The highest area is the limited probability of nitrate concentration ( Figure 8 ), despite the moderate to high susceptibility in the Marvdasht watershed. When we talk about the groundwater susceptibility model, different statistical and empirical models for predicting groundwater mineral concentrations have been reviewed over the last decades [ 75 , 76 , 77 ]. However, these susceptibility models have some limitations and assumptions, and recently data mining with machine learning approaches has been effectively popularized due to their ability to analyze the multifarious relationship between predictors and response [ 34 , 78 ].…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

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Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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“…In the AHP model, priority is given not only to the single element, but also to the cluster of elements that are often needed (Saaty 1999). In this paper, the AHP model was successfully used to assign relative importance to parameters that elucidate potential groundwater areas (Machiwal et al 2015;Jhariya et al 2016;Dasho et al 2017;Das et al 2017;Chakrabortty et al 2018;Das et al 2019). The main aim of this work is to identify the groundwater potential area of Purba Bardhaman district through the application AHP method and GIS technique.…”

Section: Introductionmentioning

confidence: 99%

Assessment of groundwater potentiality using geospatial techniques in Purba Bardhaman district, West Bengal

2020

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The word water is life, so life on this planet cannot be possible without water. Water is an essential natural resource that is a surface and groundwater device for human society. The purpose of this research is to assess the groundwater potentiality of the Purba Bardhaman district. All data (primary and secondary) are collected from different sources and analyzed in geographic information system (GIS) software to prepare thematic maps. Different geo-environmental factors like as land use and land cover, soil, lithology, rainfall and distance from the river, etc., can impact on groundwater availability directly or indirectly in Purba Bardhaman area. To identify groundwater potential zones, all these factors are composed into GIS software using multi-criteria decision analysis (MCDA) method. The groundwater potential map has been divided into five classes based on their magnitude as very high, high, medium, low and very low groundwater potential zones. It shows that the areas of very low, low, medium, high and very high groundwater potential zones are 21.54%, 35.80%, 26.47%, 10.13%, 6.06%, respectively, of the total area. Finally, validation is carried out using groundwater depth data collected from 44 drilled tube wells which are located in a scattered manner for whole Purba Bardhaman district which indicates a higher similarity with an area under curve value of 86.8%.

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“…The low slopes cause a slow drainage of water on the versants and it is possible to accumulate in micro-depressions or river meadow. In these areas there is the greatest possibility of groundwater infiltration and aquifers recharging (Chakrabortty et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

Evaluation on Groundwater Recharges Capacity Using the AHP Method - Case Study: The Moldavian Plain

Boicu¹,

Geol²,

Minea³

et al. 2019

Air and Water Components of the Environment Conference

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In the context in which the distribution of the phreatic level represents one of the main components regarding the water supply of the population, it was necessary to approach a method aimed at highlighting the areas where the underground recharging capacity is favorable or unfavorable. In the present study, this technique called AHP (Analytical Hierarchy Process) was used to highlight the distribution of groundwater recharge areas in the Moldavian Plain area by assigning weight factors to each thematic layer. In the realization of the study were used thematic layers such as drainage density, slope, land use, precipitation distribution, groundwater level, soils, lithology. For the final map, we used the weighted overlay toolbox from the ArcGis software, giving the weight factor for each thematic layer. The new thematic layer was divided into 5 classes to highlight their distribution within the study area.

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Modeling and mapping of groundwater potentiality zones using AHP and GIS technique: a case study of Raniganj Block, Paschim Bardhaman, West Bengal

Cited by 81 publications

References 71 publications

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Assessment of groundwater potentiality using geospatial techniques in Purba Bardhaman district, West Bengal

Evaluation on Groundwater Recharges Capacity Using the AHP Method - Case Study: The Moldavian Plain

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