Use of geospatial technology for delineating groundwater potential zones with an emphasis on water-table analysis in Dwarka River basin, Birbhum, India

Thapa, Raju; Gupta, Srimanta; Gupta, Arindam; Reddy, D. V.; Kaur, Harjeet

doi:10.1007/s10040-017-1683-0

Cited by 56 publications

(16 citation statements)

References 71 publications

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“…The final landslide susceptibility zonation map is also validated using SRC and PRC, which is a widely used technique for validations of the model (Kaur, Gupta, & Parkash, 2017b;Thapa et al, 2017aThapa et al, , 2017bThapa et al, , 2017cThapa et al, , 2017dThapa, Gupta, Kaur, & Mandal, 2018). The success rate of the goodness of fit of the model applied can be distinctly visualized using this validation method (Klose, Gruber, Damm, & Gerold, 2014).…”

Section: Success Rate Curve (Src) and Prediction Rate Curve (Prc)mentioning

confidence: 99%

Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ)

Kaur

Gupta

Parkash

et al. 2018

Geology, Ecology, and Landscapes

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The Sikkim state, including Gangtok, is dominated by Precambrian rocks which contain foliated schists and phyllites; slopes are therefore susceptible to frequent landslides. The recent development of roads and building structures make this region more vulnerable to landslide hazard. In this research work, landslide susceptibility zonation mapping within Gangtok Municipal Corporation (GMC) area have been carried out implementing remote sensing and GIS technique. To derive the landslide susceptibility map (LSM) of GMC, weighted overlay method (WOM) was implemented by assigning weights to various triggering factors via expert opinion. The twelve triggering factors used in the study were geology/lithology, slope morphometry, lineament density, water regime, rainfall, elevation, soil type, soil liquefaction, soil thickness, building density, relative relief, and land use/land covers (LULC). The final LSM of GMC shows that about 19.14% of the study area falls under very high landslide hazard zone and 31.78% area falls under the high category. Medium and low landslide hazard zone encompasses about 30.95% and 18.11 % of the total area, respectively. The modelgenerated LSM is validated with past reported landslide event where an overall accuracy of above 80% is observed.

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Section: Success Rate Curve (Src) and Prediction Rate Curve (Prc)mentioning

confidence: 99%

Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ)

Kaur

Gupta

Parkash

et al. 2018

Geology, Ecology, and Landscapes

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“…The HPI (heavy metal pollution index) is suitable for a comparison of the places exposed to heavy metal activity [10]. The obtained physicochemical indices may be proposed for the selection of the factors useful in hydrochemical evaluation [37], or the modelling of relations between catchment features and water quality [38]. The dependence of the concentration of the selected indices of water quality in small water reservoirs may be confirmed by the use of the catchment.…”

Section: Land Use Prognostic-regressive Method and Systematic Studimentioning

confidence: 99%

Multiannual Assessment of the Risk of Surface Water Erosion and Metal Accumulation Indices in the Flysch Stream Using the MARS Model in the Polish Outer Western Carpathians

2019

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The anthropogenic problems in richly sculpted areas (mountain and submontane) are mainly related to agricultural activity, which shapes the quality of surface waters. This paper presents an analysis of the results of the hydrochemical tests carried out in the years 2007–2018 at check-control spots. The heavy metal pollution index (HPI < 100) indicated a low risk for aquatic organisms. The statistical calculations obtained by means of multivariate adaptive regression splines (MARS) designated that the hazard quotient (HQ) index can be combined with the electrical conductivity (EC) and total dissolved substances (TDS) parameters used in the assessment of water erosion. The HQ index showed that the concentration of individual heavy metals is lower than their value in the 0.5 percentile of the value. In subsequent studies, it should be assessed whether the size of arable land has an impact on the increased concentration of heavy metals in the surface water. We recommend small retention reservoirs as a potential management measure to improve the quality of the surface water at a regional scale. This study has great potential to mitigate the degradation processes related to the insufficient storage capacity, and to promote natural water retention.

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“…Lineaments play a vital role in slope failure (Ramli et al 2010). Lineaments describe the plane/zone of weakness and most landslides can occur at this weaker zone (Kannan et al 2013;Thapa et al 2017a). A regional lineament map is developed from IRS LISS III digital data (5 m resolution).…”

Section: Lineament Densitymentioning

confidence: 99%

Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques

et al. 2019

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Landslides are common and frequent occurring phenomenon in hilly terrain during monsoon season. The primary objectives of the research work are to carry out a comprehensive analysis by quantifying the landslide susceptibility using an integrated approach of random forest (RF) with the probabilistic likelihood ratio (RF-PLR), fuzzy logic (FL) and index of entropy (IOE) in Gangtok city of Sikkim state, India. Landslide inventories are prepared based on LISS-IV (MX) satellite imagery, Google Earth and reported data of Geological Survey of India. Altogether 12 landslide conditioning factors viz. slope, elevation, curvature, aspect, land use/land cover, geology, lineament, rainfall, soil type, soil thickness, water regime and distance from road are considered as input data for geospatial modelling of landslide susceptibility. Finally, model-derived landslide susceptibility maps are classified into four hazard zones, i.e. low, medium, high and very high. To measure model compatibility model comparison is performed in ArcGIS environment and models performance is assessed by confusion matrix where RF-FL gives more accuracy of 69.36% than other two models with 9.68% and 19.35% of Type I and type II error, respectively. The outputs are validated using success and prediction rate method where, RF-PLR, RF-FL and RF-IOE show area under curve (AUC) of success and prediction rate as 76%, 67%, 83%, 78% and 85%, 80%, respectively. Additionally, the differences in model performances were analyzed by means of Wilcoxon signed rank test, where it was found that statistically differences in the performance was significant in case of RF-PLR vs. RF-FL and RF-PLR vs. RF-IOE.

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Use of geospatial technology for delineating groundwater potential zones with an emphasis on water-table analysis in Dwarka River basin, Birbhum, India

Cited by 56 publications

References 71 publications

Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ)

Knowledge-driven method: a tool for landslide susceptibility zonation (LSZ)

Multiannual Assessment of the Risk of Surface Water Erosion and Metal Accumulation Indices in the Flysch Stream Using the MARS Model in the Polish Outer Western Carpathians

Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques

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