Reconstructing snow-avalanche extent using remote sensing and dendrogeomorphology in Parâng Mountains

Meseşan, Flaviu; Man, Titus; Pop, Olimpiu; Gavrilă, Ionela-Georgiana

doi:10.1016/j.coldregions.2018.10.002

Cited by 20 publications

(9 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to acquire homogeneity with other predictive maps, and to increase the efficiency in terms of manipulation and storage, the generated high-resolution DEM was coarsened to 10 m resolution. Scaling from high-to coarse-resolution images is a common approach in spatial analysis and modeling, especially in large-scale regions, and often results in decreasing computational load and time [10,33,44,45]. Therefore, a spatial resolution of 10 m was used to calculate topography related predictive factors including topographic position index (TPI), terrain ruggedness index (TRI), topographic wetness index (TWI), length-slope (LS), relative slope position (RSP), vector ruggedness measure (VRM), aspect, slope degree, distance from stream, and profile curvature.…”

Section: Predictive Factorsmentioning

confidence: 99%

“…For example, mountainous regions are relatively active hydrologically and geophysically and there is considerable topography-driven variation in vegetation, moisture, and energy [6,7]. Mountainous environments commonly experience a wide range of natural disasters, such as avalanches [8][9][10], landslides [11][12][13][14], floods [5,[15][16][17][18], debris flows [19][20][21], soil erosion [22][23][24], and rockfalls [25][26][27]. Therefore, planners need hazard susceptibility maps to cope with natural disasters in mountainous areas, particularly flash floods, avalanches, and rockfalls [8,25,28].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

et al. 2019

View full text Add to dashboard Cite

Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models—support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.

show abstract

Section: Predictive Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Exposure of people to these extreme natural processes could be reduced and limited if predictive models based on new approaches and deeper knowledge of effective factors were employed 7 . Mountainous areas are commonly sites of snow avalanches 8 , 9 , landslides 4 , 10 , floods 11 , 12 , mudflows 13 , ice avalanches 14 , soil erosion 15 – 17 , rock falls 18 , and wildfires 19 – 24 .…”

Section: Introductionmentioning

confidence: 99%

A machine learning framework for multi-hazards modeling and mapping in a mountainous area

Yousefi

Pourghasemi

Emami

et al. 2020

Sci Rep

View full text Add to dashboard Cite

this study sought to produce an accurate multi-hazard risk map for a mountainous region of iran. the study area is in southwestern iran. the region has experienced numerous extreme natural events in recent decades. This study models the probabilities of snow avalanches, landslides, wildfires, land subsidence, and floods using machine learning models that include support vector machine (SVM), boosted regression tree (BRT), and generalized linear model (GLM). Climatic, topographic, geological, social, and morphological factors were the main input variables used. the data were obtained from several sources. The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is the most accurate for predicting landslides, land subsidence, and flood hazards in the study area. GLM is the best algorithm for wildfire mapping, and FDA is the most accurate model for predicting snow avalanche risk. The values of AUC (area under curve) for all five hazards using the best models are greater than 0.8, demonstrating that the model's predictive abilities are acceptable. A machine learning approach can prove to be very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling. the predictive maps produce valuable baselines for risk management in the study area, providing evidence to manage future human interaction with hazards. Human interactions with natural extreme events, or hazards, are increasing globally 1. Natural disasters have affected people and natural environments generating vast economic losses around the world. However, in some developed counties disasters have been decreasing since 1900 2,3. Hazard is the probability of occurrence in a specified period and within a given area of a potentially damaging of a given magnitude 4,5. The definition incorporates the concepts of location (where?), time (when, or how frequently?) and magnitude (how large?). Total risk (R) means the expected number of lives lost, person injured, damage to property, or disruption of economic activity due to a particular natural phenomenon, and is therefore the product of specific risk (RS) and elements at risk (E) 6. In addition, RS is the expected degree of loss due to a natural phenomenon. Landscapes around the world are reflections of diverse natural processes. The probabilities of extreme natural events are typically greater in more natural areas and are, in fact, extensions of natural systems. Exposure of people to these extreme natural processes could be reduced and limited if predictive models based on new approaches and deeper knowledge of effective factors were employed 7. Mountainous areas are commonly sites of snow avalanches 8,9 , landslides 4,10 , floods 11,12 , mudflows 13 , ice avalanches 14 , soil erosion 15-17 , rock falls 18 , and wildfires 19-24. Most studies focus on a single hazard, even when there are multiple hazardous processes affecting the same landscapes 8,25-30. However, hazards sometimes interact with each other. Sometimes, the mitigation of one

show abstract

“…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%

Mass wasting susceptibility assessment of snow avalanches using machine learning models

Choubin

Borji

Hosseini

et al. 2020

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

Reconstructing snow-avalanche extent using remote sensing and dendrogeomorphology in Parâng Mountains

Cited by 20 publications

References 62 publications

Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

A machine learning framework for multi-hazards modeling and mapping in a mountainous area

Mass wasting susceptibility assessment of snow avalanches using machine learning models

Contact Info

Product

Resources

About