GIS-based MCDA–AHP modelling for avalanche susceptibility mapping of Nubra valley region, Indian Himalaya

Kumar, Satish; Srivastava, Pankaj Kumar; Snehmani,

doi:10.1080/10106049.2016.1206626

Cited by 47 publications

(26 citation statements)

References 34 publications

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“…Snow avalanches killed four people and injured 28 more in 2015-2018. Collecting and documenting previous snow avalanches is important for modeling the snow avalanche hazard [49,50]. Avalanche occurrence is the dependent variable in the modeling process.…”

Section: Study Areamentioning

confidence: 99%

“…Little is known about the efficiency of these models, and it is not clear which geomorphometric and topohydrological factors affect snow avalanche distribution and activity. Recently, Kumar et al [49] used a simple data-mining method, probabilistic occurrence ratio (POR) (also known as a frequency ratio), and a snow avalanche inventory to prepare a snow avalanche hazard map in the Lahaul region of the western Himalayas. Their results indicated that this simple model performed well in the task of identifying snow avalanche-prone areas.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

et al. 2019

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Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

et al. 2019

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“…One more advantage of the method, is that AHP incorporates both qualitative and quantitative aspects of decision-making [4,84,85] and therefore, it constitutes an efficient tool for controlling the consistency of the evaluation criteria and the alternatives proposed [79]. Additionally, this comprehensive view reduces potential hindrances in the decision-making process [68].…”

Section: Multi-criteria Analysis-analytical Hierarchy Process (Ahp)mentioning

confidence: 99%

“…This method has been proven to be extremely useful for entering a not common lifestyle and recognizing variables influencing it [3]. Analytical Hierarchal Process allows an easy quantification of qualitative variables [4]. The combination of those two methods is used to develop a participative process to recognize environmental variables enhancing or hindering cycling tourism with the active contribution of cycle tourists, thus obtaining a better understanding about which policies are more capable to foster and attract cycling tourism in Greece.…”

Section: Introductionmentioning

confidence: 99%

Exploring Motivators and Deterrents of Cycling Tourism Using Qualitative Social Research Methods and Participative Analytical Hierarchy Process (AHP)

et al. 2020

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Cycling tourism is an environmentally friendly way to explore a destination. Nowadays, it is becoming more and more popular worldwide, since it permits an in-depth communication both with nature and people. In this context, the aim of this paper is to explore, in detail, mainly the environmental motivators of cycling tourists, and their significance, in Greece, by using qualitative social research (in-depth interviews and focus groups) and participative analytical hierarchical process. The results show that regarding physical environment, the element which attracts cycle tourists the most is the variety of scenic views along their route. Existence of lakes, rivers and beaches were also valued. On the contrary, “Biodiversity” and “Untouched natural landscapes” were ranked low as a description of the quality of the natural environment. However, the rise of cycling tourism depends considerably on the appropriate infrastructure, namely roads with low slopes, appropriate quality of surfaces and cycling infrastructure. Planners, policy-makers, and competent authorities should give emphasis on the critical factors in order to enhance cycling tourism, thus resulting in positive multiplying effects such as environmental protection and economic growth.

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“…For this assessment, a set of factors must first be determined and then evaluated according to their importance [35]; however, it is very difficult to overcome the complexities of the phenomenon. GIS-based multi-criteria decision analysis (MCDA) plays an important role in addressing the challenges encountered in vulnerability analyses [36][37][38][39]. This study aimed to explore the use and forecasting capabilities of the GIS-based MCDA model, which is used the first time for determining vulnerable glacier zones.…”

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confidence: 99%

A GIS-Based Multi-Criteria Decision Analysis Model for Determining Glacier Vulnerability

Yalçın

2020

IJGI

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Investigating the causes of the spatial heterogeneity of glacial changes offers vital information about glacial behavior and provides forecasting ability to define where glacier retreat may occur in the future. This study was designed to determine the spatial distribution of Ağrı Mountain glacier vulnerability. The main goal of the current study was to assess the forecasting capabilities of Geographical Information System (GIS)-based Multi-Criteria Decision Analysis (MCDA) for determining the location of the mountain glacier retreat. To estimate the glacier retreat, the following criteria were selected: elevation, aspect, slope, direction, and glacier surface temperature anomaly (GSTA). The entropy method was used for weighting the criteria for the evaluation of the vulnerable areas of the glacier. The results of this method clearly indicate a strong relationship between GSTA, direction, and elevation criteria and glacier retreat. The glacier vulnerability map was created by synthesizing criteria layers with their weights. The vulnerability map provided a consistency of 77.8% in the short term and 92.1% in the long term. In the study, the priority melting zones were determined and glacial retreat locations were forecasted in 10-year periods.

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GIS-based MCDA–AHP modelling for avalanche susceptibility mapping of Nubra valley region, Indian Himalaya

Cited by 47 publications

References 34 publications

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Exploring Motivators and Deterrents of Cycling Tourism Using Qualitative Social Research Methods and Participative Analytical Hierarchy Process (AHP)

A GIS-Based Multi-Criteria Decision Analysis Model for Determining Glacier Vulnerability

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