Assessment of groundwater recharge and its potential zone identification in groundwater-stressed Goghat-I block of Hugli District, West Bengal, India

Das, Biswajit; Pal, Subodh Chandra

doi:10.1007/s10668-019-00457-7

Cited by 66 publications

(20 citation statements)

References 40 publications

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“…In this plain region, they are the most effective. Remote sensing (RS) and GIS techniques (Krishnamurthy et al 1996;Pothiraj and Rajagopalan 2013;Sar et al 2015;Anbazhagan and Jothibasu 2016;Jothibasu and Anbazhagan 2017;Choudhari et al 2018;Das and Pal 2019a, b) are very much useful to delineate the groundwater potential zone. Many researchers in India and aboard have been successfully using this technique to identify groundwater potential zone.…”

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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Section: Introductionmentioning

confidence: 99%

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

2020

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“…The results of the flood susceptibility maps of the Talar watershed show that low elevation areas of the watershed, which often have a low slope and are close to the watershed outlet, are highly sensitive to floods. The importance of altitude has been proven in other studies in the field of flood risk modeling in other studies (Khosravi et al 2019;Sahana and Patel 2019;Das and Pal 2020;Yariyan et al 2020;Kalantar et al 2021). In accordance with the present results, Kalantar et al (2021) demonstrated that altitude and distance from the river are the most important variables in flood hazard modeling in Brisbane, Australia.…”

Section: Discussionmentioning

confidence: 85%

Evaluation of various boosting ensemble algorithms for predicting flood hazard susceptibility areas

Pham

Pal

Chakrabortty

et al. 2021

Geomatics, Natural Hazards and Risk

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The purpose of the present study was to predict the areas affected by flood hazard in the Talar watershed, Mazandaran province, Iran, using Adaptive Boosting (AdaBoost), Boosted Generalized Linear Models (BGLM), Extreme Gradient Boosting (XGB) ensemble models, and the novel ensemble framework of deep decision trees include the Deep Boosting (DB) model. For this purpose, 14 flood conditioning variables were used as independent variables in flood hazard modeling. In addition, 130 flood points in the region were identified by field visits and available flood information, which were used as the dependent variable in modeling. The results showed that all used models have a good efficiency in predicting flood hazard. The area under curve (AUC) of BGLM, XGB, AdaBoost and DB models were 0.88, 0.87, 0.89 and 0.91, respectively, which indicated the highest efficiency of the DB model in flood hazard modeling in the study area. Relative importance of the variables showed that they have different effects in each model. Altitude and distance from the river are more important than other variables. However, these two variables have been selected as the most important variables based on machine learning models, but other variables may be influential in flood hazards.

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“…Geographic information systems (GIS) and remote sensing (RS) techniques are very useful for modeling groundwater depletion, recharge and stress zones (Anderson et al 2015 ; Rodell et al 2009 ; Famiglietti et al 2011 ; Gleeson and Wada 2013 ; Voss et al 2013 ; Castle et al 2014 ; Da Costa et al 2019 ; Das and Pal 2019a , b ). In particular, satellite-based groundwater data provide a primary investigation of groundwater conditions.…”

Section: Introductionmentioning

confidence: 99%

Recognition of district-wise groundwater stress zones using the GLDAS-2 catchment land surface model during lean season in the Indian state of West Bengal

et al. 2021

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Assessment of groundwater recharge and its potential zone identification in groundwater-stressed Goghat-I block of Hugli District, West Bengal, India

Cited by 66 publications

References 40 publications

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

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

Evaluation of various boosting ensemble algorithms for predicting flood hazard susceptibility areas

Recognition of district-wise groundwater stress zones using the GLDAS-2 catchment land surface model during lean season in the Indian state of West Bengal

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