Comparative evaluation of various approaches for landslide hazard zoning: a critical review in Indian perspectives

Kaur, Harjeet; Gupta, Srimanta; Parkash, Surya

doi:10.1007/s41324-017-0105-7

Cited by 40 publications

(16 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2001, nearly 40 individuals died in the Amboori of Kerala state of India; in 2013, a landslide occurred in Kedarnath of Uttarakhand state of India and more than 5000 people died; and in 2014 in the Pune of Maharashtra state of India, over 100 individuals were found to be missing after the landslide (Figure 1) [10,11]. Figure 1 depicts the locations of landslide events in India and the fatalities that occurred at such locations-an estimated 12.6% of land area of India and approximately 4.5 million USD worth economic damage [12]. Based on our analysis of the NASA data, we found that a total of 958 landslide events occurred between 2007 and 2015 that caused 6779 human fatalities (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

View full text Add to dashboard Cite

Landslides have multidimensional effects on the socioeconomic as well as environmental conditions of the impacted areas. The aim of this study is the spatial prediction of landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) as base classifier in the northern part of the Pithoragarh district, Uttarakhand, Himalaya, India. To construct the database, ten conditioning factors and a total of 103 landslide locations with a ratio of 70/30 were used. The significant factors were determined by chi-square attribute evaluation (CSEA) technique. The validity of the hybrid models was assessed by true positive rate (TP Rate), false positive rate (FP Rate), recall (sensitivity), precision, F-measure and area under the receiver operatic characteristic curve (AUC). Results concluded that land cover was the most important factor while curvature had no effect on landslide occurrence in the study area and it was removed from the modelling process. Additionally, results indicated that although all ensemble models enhanced the power prediction of the ADTree classifier (AUC training = 0.859; AUC validation = 0.813); however, the RS ensemble model (AUC training = 0.883; AUC validation = 0.842) outperformed and outclassed the RF (AUC training = 0.871; AUC validation = 0.840), and the BA (AUC training = 0.865; AUC validation = 0.836) ensemble model. The obtained results would be helpful for recognizing the landslide prone areas in future to better manage and decrease the damage and negative impacts on the environment.

show abstract

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

View full text Add to dashboard Cite

show abstract

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Landslide susceptibility zonation has been carried out widely all around the world to demarcate landslide vulnerable areas using remote sensing and Geographical Information System (Anbalagan et al 2015;Kannan, Saranathan, and Anbalagan 2015). Landslide susceptibility mapping is done by two process namely qualitative (knowledge-driven) and quantitative (statistical) techniques (Kaur et al 2017a). Expert knowledge develops through a combination of field experiences and theoretical understanding of physical processes.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Comparative evaluation of various approaches for landslide hazard zoning: a critical review in Indian perspectives

Cited by 40 publications

References 32 publications

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

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

Contact Info

Product

Resources

About