Geo-spatial Modeling for Automated Demarcation of Snow Avalanche Hazard Areas Using Landsat-8 Satellite Images and In Situ Data

Singh, Dhiraj Kumar; Mishra, V. D.; Gusain, Hemendra Singh; Gupta, Neena; Singh, Arun Kumar

doi:10.1007/s12524-018-00936-w

Cited by 28 publications

(19 citation statements)

References 36 publications

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“…The naïve Bayes (NB) ML model is a widely used classifier, primarily in data mining studies. The popularity of this model is due to the simplicity of its construction and runtime [95,96]. Furthermore, the NB model is robust and is less affected by noise and irrelevant attributes [97].…”

Section: Characteristic Functionmentioning

confidence: 99%

“…Therefore, the GAM generates a flexible classification of the dependence of the response on the covariates, which makes it practical for analysis of nonlinear responses to changing site conditions. It is, consequently, useful for spatial problems like natural hazard susceptibility modeling and mapping [96]. In the GAM model, the additive predictor η(X) is used as an alternative to the common linear predictor in the GLM.…”

Section: (4) Generalized Additive Model (Gam)mentioning

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: Characteristic Functionmentioning

confidence: 99%

Section: (4) Generalized Additive Model (Gam)mentioning

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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“…The complexity of avalanche models has been discussed in many studies 27 – 31 . The snowpack, meteorology, terrain, and slope characteristics are the predominant contributing factors initiating the avalanche movement and propagation, and the debris deposition 2 , 32 , 33 . Based on the interaction of these factors, the motion and run out of snow and eventually, the avalanche formation and propagation can be modeled 2 , 32 , 34 – 36 .…”

Section: Introductionmentioning

confidence: 99%

“…Various numerical methods for modeling the avalanche flow dynamics 37 – 41 , as well as statistical approaches for processing historical database information and climatological data sets 15 , 42 – 45 , have been proposed to predict the hazard susceptibility mapping. The models have been enhanced with the involvement of recent advanced technologies of geographic information systems (GIS), remote sensing (RS), Satellite image processing, and artificial intelligence (AI) methods and applications 2 , 29 , 46 – 49 . GIS is a powerful tool for the construction of terrain’s geographical database to support building accurate prediction and decision-making models with high precision for terrain visualization 50 – 54 .…”

Section: Introductionmentioning

confidence: 99%

“…GIS is a powerful tool for the construction of terrain’s geographical database to support building accurate prediction and decision-making models with high precision for terrain visualization 50 – 54 . Also, RS contributes to data collection through remotely sensing the inaccessible mountainous areas, which are a real asset in replacing costly and slow ground data collection systems without disturbing the snow cover 2 , 29 , 55 , 56 .…”

Section: Introductionmentioning

confidence: 99%

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Mass wasting susceptibility assessment of snow avalanches using machine learning models

Choubin

Borji

Hosseini

et al. 2020

Sci Rep

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Snow avalanche is among the most harmful natural hazards with major socioeconomic and environmental destruction in the cold and mountainous regions. The devastating propagation and accumulation of the snow avalanche debris and mass wasting of surface rocks and vegetation particles threaten human life, transportation networks, built environments, ecosystems, and water resources. Susceptibility assessment of snow avalanche hazardous areas is of utmost importance for mitigation and development of land-use policies. This research evaluates the performance of the well-known machine learning methods, i.e., generalized additive model (GAM), multivariate adaptive regression spline (MARS), boosted regression trees (BRT), and support vector machine (SVM), in modeling the mass wasting hazard induced by snow avalanches. The key features are identified by the recursive feature elimination (RFE) method and used for the model calibration. The results indicated a good performance of the modeling process (Accuracy > 0.88, Kappa > 0.76, Precision > 0.84, Recall > 0.86, and AUC > 0.89), which the SVM model highlighted superior performance than others. Sensitivity analysis demonstrated that the topographic position index (TPI) and distance to stream (DTS) were the most important variables which had more contribution in producing the susceptibility maps.

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An application of multiple‐point statistics downscaling approach over North‐West Himalayas in avalanche‐prone areas

Singhal

Jha

2021

Intl Journal of Climatology

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Weather data at fine spatial resolution in the Himalayan region are important for assessing various hydrological events such as avalanches. In recent years, national agencies have been exploring the use of Weather Research and Forecasting (WRF) model in this region. Setting up a WRF model with fine spatial grids in the Himalayan region is challenging due to large topographical variations and associated computational cost. In this study, we use High Asia Refined analysis (HAR) dataset to set up a Multiple-Point Statistics (MPS) based downscaling approach using the hindcast data of years 2001-2012 at both coarse and fine spatial grids. A total of 11 covariates are used in the study belonging to various environmental factors such as weather, location, topography and vegetation. We generate multiple realizations of precipitation and

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Geo-spatial Modeling for Automated Demarcation of Snow Avalanche Hazard Areas Using Landsat-8 Satellite Images and In Situ Data

Cited by 28 publications

References 36 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

Mass wasting susceptibility assessment of snow avalanches using machine learning models

An application of multiple‐point statistics downscaling approach over North‐West Himalayas in avalanche‐prone areas

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