Landslide Susceptibility Mapping, Vulnerability and Risk Assessment for Development of Early Warning Systems in India

Wadhawan, S. K.

doi:10.1007/978-3-319-77377-3_8

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…Similar hydrological, topographical, and environmental variables are not appropriate for modeling and may be varied according to the geographic setting of the regions. In this research, the LCFs were chosen based on the existing literature [10,40,41] and applying the one factor selection method, i.e., Relief-F. For this study, sixteen LCFs were selected for landslide susceptibility mapping. Among the selected factors, rainfall and earthquake zone are landslide triggering factors and the rest are landslide causative factors.…”

Section: Preparation Of the Landslide Causative Factors (Lcfs)mentioning

confidence: 99%

“…LU/LC Supervised classification (Maximum likelihood) [10] Topographic LCFs were extracted from a PALSAR DEM with the resolution of 12.5 × 12.5 m using 'surface' tool in ArcGIS 10.3.1 software. These LCFs are altitude, slope gradient, slope aspect, curvature and stream power index.…”

Section: Ndvi = Nir−redmentioning

confidence: 99%

“…the Uttarakhand earthquake in 2017, the Chamoli earthquake in 1999, and the Sikkim earthquake in 2011 [6][7][8][9]. Wadhawan [10] mentioned that the mountainous region of Konkan and the Nilgiri ranges in the south and south-western India and the Western Ghats are severely vulnerable to landslides and mass movements. Other than seismic tremors and rainstorm, human interventions and unplanned encroachment, i.e., construction of roads, civil structures etc., disturb the resistance of landmass and accelerate the rate of landslide occurrence.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya

Saha

Hembram

et al. 2020

Applied Sciences

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Landslides are known as the world’s most dangerous threat in mountainous regions and pose a critical obstacle for both economic and infrastructural progress. It is, therefore, quite relevant to discuss the pattern of spatial incidence of this phenomenon. The current research manifests a set of individual and ensemble of machine learning and probabilistic approaches like an artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LR), and their ensembles such as ANN-RF, ANN-SVM, SVM-RF, SVM-LR, LR-RF, LR-ANN, ANN-LR-RF, ANN-RF-SVM, ANN-SVM-LR, RF-SVM-LR, and ANN-RF-SVM-LR for mapping landslide susceptibility in Rudraprayag district of Garhwal Himalaya, India. A landslide inventory map along with sixteen landslide conditioning factors (LCFs) was used. Randomly partitioned sets of 70%:30% were used to ascertain the goodness of fit and predictive ability of the models. The contribution of LCFs was analyzed using the RF model. The altitude and drainage density were found to be the responsible factors in causing the landslide in the study area according to the RF model. The robustness of models was assessed through three threshold dependent measures, i.e., receiver operating characteristic (ROC), precision and accuracy, and two threshold independent measures, i.e., mean-absolute-error (MAE) and root-mean-square-error (RMSE). Finally, using the compound factor (CF) method, the models were prioritized based on the results of the validation methods to choose best model. Results show that ANN-RF-LR indicated a realistic finding, concentrating only on 17.74% of the study area as highly susceptible to landslide. The ANN-RF-LR ensemble demonstrated the highest goodness of fit and predictive capacity with respective values of 87.83% (area under the success rate curve) and 93.98% (area under prediction rate curve), and the highest robustness correspondingly. These attempts will play a significant role in ensemble modeling, in building reliable and comprehensive models. The proposed ANN-RF-LR ensemble model may be used in the other geographic areas having similar geo-environmental conditions. It may also be used in other types of geo-hazard modeling.

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Section: Preparation Of the Landslide Causative Factors (Lcfs)mentioning

confidence: 99%

Section: Ndvi = Nir−redmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya

Saha

Hembram

et al. 2020

Applied Sciences

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“…An investigation from the past two decades (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) has shown that up to 4.8 million people have been affected by landslides, and over 18 thousand people have lost their lives (CRED 2018). Constructing early warning systems (Wadhawan 2019;Yang et al 2019), landslide susceptibility (Catani et al 2013;Di Traglia et al 2018;Fang et al 2021), landslide hazard (Guzzetti et al 2005) and landslide risk zonation (Guo et al 2020;Xiao et al 2020a;Li et al 2021) are effective approaches to managing landslide-prone areas. Important aspects of such landslide hazard assessments are landslide magnitude-frequency distributions (Catani et al 2005;Fell et al 2008a;Guzzetti et al 2012;Wang et al 2017;Zhou et al 2020;Jia et al 2021).…”

Section: Introductionmentioning

confidence: 99%

The SWADE model for landslide dating in time series of optical satellite imagery

Jong

Deijns

et al. 2023

Landslides

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Landslides are destructive natural hazards that cause substantial loss of life and impact on natural and built environments. Landslide frequencies are important inputs for hazard assessments. However, dating landslides in remote areas is often challenging. We propose a novel landslide dating technique based on Segmented WAvelet-DEnoising and stepwise linear fitting (SWADE), using the Landsat archive (1985–2017). SWADE employs the principle that vegetation is often removed by landsliding in vegetated areas, causing a temporal decrease in normalized difference vegetation index (NDVI). The applicability of SWADE and two previously published methods for landslide dating, harmonic modelling and LandTrendr, are evaluated using 66 known landslides in the Buckinghorse River area, northeastern British Columbia, Canada. SWADE identifies sudden changes of NDVI values in the time series and this may result in one or more probable landslide occurrence dates. The most-probable date range identified by SWADE detects 52% of the landslides within a maximum error of 1 year, and 62% of the landslides within a maximum error of 2 years. Comparatively, these numbers increase to 68% and 80% when including the two most-probable landslide date ranges, respectively. Harmonic modelling detects 79% of the landslides with a maximum error of 1 year, and 82% of the landslides with a maximum error of 2 years, but requires expert judgement and a well-developed seasonal vegetation cycle in contrast to SWADE. LandTrendr, originally developed for mapping deforestation, only detects 42% of landslides within a maximum error of 2 years. SWADE provides a promising fully automatic method for landslide dating, which can contribute to constructing landslide frequency-magnitude distributions in remote areas.

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“…In total 15% or 0.49 million km 2 of the terrain in India are susceptible to landslide are prone to landslide (Walde et al, 2017). The most affected parts of landslide in India are Himalayas, Westerns Ghats, Nilgris, and Vindhyas (Chandrasekaran, Elayaraja, & Renugadevi, 2013;Mahanta, Sathe, & Mahagaonkar, 2016;Ramakrishnan, Singh, Verma, Gulati, & Tiwari, 2012;Sathe, Goswami, & Mahanta, 2019;Sathe, Mahanta, & Mishra, 2018;Thennavan, Ganapathy, Sekaran, & Rajawat, 2016;Vasantha Kumar & Bhagavanulu, 2008;Wadhawan, 2018). On the basis of landslide hazard map of states of India, like Sikkim and Mizoram, have found that they are falling under very high to severe hazard classes (Punmia and Jain 1970).…”

Section: Introductionmentioning

confidence: 99%

A hill slope failure analysis: A case study of Malingoan village, Maharashtra, India

Shah¹,

Sathe²,

Bhagawati³

et al. 2019

Geology, Ecology, and Landscapes

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Landslide catastrophe happen on 30 July 2014 has buried almost 140 peoples under a loose soil mass in Malingaon village of Pune district, Maharashtra, India. Soil samples from a hill slope sections were collected (viz. bottom, middle, and top) for determination of the soil properties and slope stability. Mainly excess rainfall triggered the land slide, which directly affected on geotechnical properties of soil. The results showed that the hill slope was unstable with factor of safety less than one and which is prone to failure. The land use and land cover map (viz. before and after of land slide) analysis have inferred that the hill slope was unstable. The unscientific way of agriculture practices method and conversion of nonagricultural land into agricultural usage was the major cause for land slide in the study area. This study provides a technically viable solution method to avoid such disaster in same topographical features.

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Landslide Susceptibility Mapping, Vulnerability and Risk Assessment for Development of Early Warning Systems in India

Cited by 7 publications

References 32 publications

Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya

Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya

The SWADE model for landslide dating in time series of optical satellite imagery

A hill slope failure analysis: A case study of Malingoan village, Maharashtra, India

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