Geospatial modelling of flood susceptibility pattern in a subtropical area of West Bengal, India

Kaur, Harjeet; Gupta, Srimanta; Parkash, Surya; Thapa, Raju; Mandal, Rumpa

doi:10.1007/s12665-017-6667-9

Cited by 31 publications

(17 citation statements)

References 38 publications

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“…Landslide events that have occurred in the study area, i.e., from the year 2000 to 2015, have been considered for validation (Kaur et al, 2017a). The final landslide susceptibility zonation map generated is overlain by recorded landslide events location ( Figure 6).…”

Section: Superimposition Of Reported Landslidesmentioning

confidence: 99%

“…In the hilly terrain of India including the Himalayas and the Western Ghats, landslides have been a major and widely spread natural disaster. In India, landslides cause approximately US$ 4,500,000 economic damage and 2185 loss of life (Kaur, Gupta, Parkash, Thapa, & Mandal, 2017a). Sikkim Himalayas is a very hot bed for natural hazard especially for landslide and earthquake (Sikkim State Disaster Management Authority, 2012).…”

Section: Introductionmentioning

confidence: 99%

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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: Superimposition Of Reported Landslidesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kaur

Gupta

Parkash

et al. 2018

Geology, Ecology, and Landscapes

Self Cite

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“…The landslide susceptibility index values are calculated in the range of 0.001-1.00. Natural jenk classification method is used to classify the resultant map into four classes namely low, medium, high and very high (Pourghasemi et al 2012;Kaur et al 2017bKaur et al , 2018Thapa et al 2017b). Finally, the landslide susceptibility map of the RF-PLR model is obtained.…”

Section: Rf-plr Modelmentioning

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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“…SRC constructed from the training dataset gives the prediction map but it does not convey the accuracy of prediction. Hence, to evaluate the future prediction accuracy of the model prediction rate curve (PRC) is implemented (Chung and Fabbri, 2003;Kaur et al, 2017Kaur et al, , 2008Thapa et al, 2017c). In PRC plotting the cumulative percentage of susceptible areas on the x-axis is compared against the cumulative percentage of nitrate occurrence in the test dataset on the y-axis.…”

Section: Success and Prediction Ratementioning

confidence: 99%

Sensitivity analysis and mapping the potential groundwater vulnerability zones in Birbhum district, India: A comparative approach between vulnerability models

Thapa¹,

Gupta

Guin³

et al. 2018

Water Science

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The assessment of groundwater vulnerability is essential especially in developing areas, where agriculture is the main source of the population. In the present study, four different overlay and index method, namely, DRASTIC, modified DRASTIC, pesticide DRASTIC and modified pesticide DRASTIC are implemented with a view to identifying the most appropriate method that predicts the vulnerable zone to groundwater pollution. Sensitivity analysis reveals that net recharge is the most influential parameter of the vulnerability index. Cross comparison of model output shows the highest similarity of 97% is observed between drastic and modified drastic while the maximum difference in models prediction of 49% is observed between modified drastic and pesticide drastic. Reported nitrate concentrations in groundwater are considered for validation of model-generated final output map. The prediction power of the models are assessed using success and prediction rate method and it highlights DRASTIC model as the most suitable model with 89.69% and 84.54% of the area under area under the curve (AUC) for success and prediction rate respectively.

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Geospatial modelling of flood susceptibility pattern in a subtropical area of West Bengal, India

Cited by 31 publications

References 38 publications

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

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

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

Sensitivity analysis and mapping the potential groundwater vulnerability zones in Birbhum district, India: A comparative approach between vulnerability models

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