Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan

Dikshit, Abhirup; Sarkar, Raju; Pradhan, Biswajeet; Jena, Ratiranjan; Drukpa, Dowchu; Alamri, Abdullah

doi:10.3390/w12010267

Cited by 37 publications

(20 citation statements)

References 81 publications

(111 reference statements)

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“…Landslide susceptibility may be defined as the likelihood that a landslide will occur in a given area or at a specific site [14]. Maps that show the propensity of an area to slope failure are termed "landslide susceptibility maps" [15,16]. These maps help land-use managers and other government officials to proactively reduce future losses from landslides [17].…”

Section: Introductionmentioning

confidence: 99%

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Nhu

Shirzadi

Shahabi

et al. 2020

IJERPH

207

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Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

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Section: Introductionmentioning

confidence: 99%

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Nhu

Shirzadi

Shahabi

et al. 2020

IJERPH

207

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“…The temporal probability was calculated using the rainfall threshold determined by Dikshit et al [26] using the Poisson probability model. Readers are referred to [3,39,40] for the detailed explanation on determining the temporal landslide probability.…”

Section: Methodsmentioning

confidence: 99%

“…The Himalayan region experiences very high rainfall and contributes over 70% of global fatal landslides [2]. Landslides in the Himalayas occur mainly due to monsoonal precipitation leading to huge financial loss and often disrupting the only livelihood of people [3,4]. The global fatal landslide database developed by [2] shows a total of 64 deaths in Bhutan due to nonseismic landslide events from 2004 to 2017, but the actual number of deaths is expected to be very high.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of the landslide incidences are triggered due to rainfall, therefore we have focused solely on rainfall-induced landslides. The previous research in Bhutan has been towards forecasting, which led to the development of several thresholds using various statistical [3,25,26] and probabilistic [27] approaches. However, landslide mitigation and understanding the elements at risk are yet to be conducted for any part of Bhutan.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Landslide Risk Assessment at Phuentsholing, Bhutan

et al. 2020

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Landslides are one of the most destructive and most recurring natural calamities in the Himalayan region. Their occurrence leads to immense damage to infrastructure and loss of land, human lives, and livestock. One of the most affected regions is the Bhutan Himalayas, where the majority of the landslides are rainfall-induced. The present study aims to determine the hazard and risk associated with rainfall-induced landslides for the Phuentsholing region located in the southwestern part of the Bhutan Himalayas. The work involves developing a landslide risk map using hazard and vulnerability maps utilizing landslide records from 2004 to 2014. The landslide hazard map was generated by determining spatial and temporal probabilities for the study region. The spatial probability was computed by analyzing the landslide contributing factors like geology, slope, elevation, rainfall, and vegetation based on comprehensive field study and expertise about the area. The contributing factors were divided into various classes and the percentage of landslide occurrence under each class was calculated to understand its contributing significance. Thereafter, a weighted linear combination approach was used in a GIS environment to develop the spatial probability map which was multiplied with temporal probabilities based on regional rainfall thresholds already determined for the region. Consequently, vulnerability assessment was conducted using key elements at risk (population, land use/land cover, proximity to road, proximity to stream) and the weights were provided based on expert judgment and comprehensive field study. Finally, risk was determined and the various regions in the study area were categorized as high, medium, and low risk. Such a study is necessary for low-economic countries like Bhutan which suffers from unavailability of extensive data and research. The study is conducted for a specific region but can be extended to other areas around the investigated area. The tool can serve as an indicator for the civil authorities to analyze the risk posed by landslides due to the rapid infrastructure development in the region.

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“…The analysis of precipitation for landslide occurrence can be performed by estimating minimum rainfall conditions, sub surface monitoring, or slope stability analysis. The minimum rainfall conditions, also known as thresholds, can be classified as empirical and physical approaches [40][41][42]. Physical models assess the relationship between rainfall conditions and hydrological conditions of the soil, which affects slope stability.…”

Section: Landslide Forecastingmentioning

confidence: 99%

Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review

et al. 2020

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Landslides are one of the most devastating and recurring natural disasters and have affected several mountainous regions across the globe. The Indian Himalayan region is no exception to landslide incidences affecting key economic sectors such as transportation and agriculture and often leading to loss of lives. As reflected in the global landslide dataset, most of the landslides in this region are rainfall triggered. The region is prone to 15% of the global rainfall-induced landslides, and thereby a review of the studies in the region is inevitable. The high exposure to landslide risk has made the Indian Himalayas receive growing attention by the landslides community. A review of landslides studies conducted in this region is therefore important to provide a general picture of the state-of-the-art, a reference point for researchers and practitioners working in this region for the first time, and a summary of the improvements most urgently needed to better address landslide hazard research and management. This article focuses on various studies ranging from forecasting and monitoring to hazard and susceptibility analysis. The various factors used to analyze landslide are also studied for various landslide zones in the region. The analysis reveals that there are several avenues where significant research work is needed such as the inclusion of climate change factors or the acquisition of basic data of highest quality to be used as input data for computational models. In addition, the review reveals that, despite the entire region being highly landslide prone, most of the studies have focused on few regions and large areas have been neglected. The aim of the review is to provide a reference for stakeholders and researchers who are currently or looking to work in the Indian Himalayas, to highlight the shortcomings and the points of strength of the research being conducted, and to provide a contribution in addressing the future developments most urgently needed to obtain a consistent advance in landslide risk reduction of the area.

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Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan

Cited by 37 publications

References 81 publications

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Spatial Landslide Risk Assessment at Phuentsholing, Bhutan

Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review

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