GIS-based spatial modeling of snow avalanches using four novel ensemble models

Yariyan, Peyman; Avand, Mohammadtaghi; Abbaspour, Rahim Ali; Karami, Mohammadreza; Tiefenbacher, John P.

doi:10.1016/j.scitotenv.2020.141008

Cited by 59 publications

(26 citation statements)

References 65 publications

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“…The ROC curve is a graphical representation of the balance between the negative and positive error rates for each possible value and is based on the set of pair points (X and Y) calculated using Equations (22) and (23). The X and Y were obtained from the comparison matrix and varied between 0 and 1 (Yariyan et al 2020a). The area under the ROC curve is called the area under the curve (AUC).…”

Section: Models Validationmentioning

confidence: 99%

Flood susceptibility mapping using an improved analytic network process with statistical models

Yariyan

Avand

Abbaspour

et al. 2020

Geomatics, Natural Hazards and Risk

Self Cite

105

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Flooding is a natural disaster that causes considerable damage to different sectors and severely affects economic and social activities. The city of Saqqez in Iran is susceptible to flooding due to its specific environmental characteristics. Therefore, susceptibility and vulnerability mapping are essential for comprehensive management to reduce the harmful effects of flooding. The primary purpose of this study is to combine the Analytic Network Process (ANP) decision-making method and the statistical models of Frequency Ratio (FR), Evidential Belief Function (EBF), and Ordered Weight Average (OWA) for flood susceptibility mapping in Saqqez City in Kurdistan Province, Iran. The frequency ratio method was used instead of expert opinions to weight the criteria in the ANP. The ten factors influencing flood susceptibility in the study area are slope, rainfall, slope length, topographic wetness index, slope aspect, altitude, curvature, distance from river, geology, and land use/land cover. We identified 42 flood points in the area, 70% of which was used for modelling, and the remaining 30% was used to validate the models. The Receiver Operating Characteristic (ROC) curve was used to evaluate the results. The area under the curve obtained from the ROC curve indicates a superior performance of the ANP and EBF hybrid model (ANP-EBF) with 95.1% efficiency compared to the combination of ANP and FR (ANP-FR) with 91% and ANP and OWA (ANP-OWA) with 89.6% efficiency.

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Section: Models Validationmentioning

confidence: 99%

Flood susceptibility mapping using an improved analytic network process with statistical models

Yariyan

Avand

Abbaspour

et al. 2020

Geomatics, Natural Hazards and Risk

Self Cite

105

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“…Sirvan river basin with an area about 13,400 km2 is located between 45°59 0 to 47°22 0 East and 34°50 0 to 35°05 0 North (northern and eastern hemisphere) (Yariyan et al 2020;Balist et al 2022) covering parts of the Kurdistan and Kermanshah provinces of Iran. This basin contains 11 sub-basins (Figure 1).…”

Section: Description Of the Study Areamentioning

confidence: 99%

Modeling the supply, demand, and stress of water resources using ecosystem services concept in Sirvan River Basin (Kurdistan-Iran)

et al. 2021

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Water resources modeling can provide valuable information to planners. In this respect, water yield is an ecosystem service with significant roles in the sustainability of societies and ecosystems. The present study aimed to model the supply and demand of water resources and identify their scarcity and stress in the Sirvan river basin. For this purpose, we employed the ecosystem services concept as new thinking in earth sciences and using soil, climate, and land use data. Firstly, the Landsat satellite images of 2019 were prepared after different corrections, and the land use map was produced. Then, precipitation, evapotranspiration, root restricting layer depth, and evapotranspiration coefficients of the land uses were prepared and modeled in InVEST 3.8.9 software environment. The findings indicated that the water yield in this river basin is 5,381 million m3, with sub-basins 5, 11, and 1 having the highest water yield per year and sub-basin 2 having the lowest water yield. Moreover, sub-basins 5 and 11 had the highest water consumption. Based on the estimated water scarcity and stress index, sub-basin 8 has experienced water scarcity and sub-basin 4 water stress. We conclude that applying the InVEST Water Yield model to assess water resource status at the basin and sub-basins level can provide suitable results for planning.

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“…However, this approach only covers high elevations areas while our study aims to detect avalanches proximate to local communities at lower elevations (typically valleys). Manual and visual approaches, despite the time consuming process, can also be applied to detect avalanches using high resolution images (e.g., SPOT-6), mid-resolution (e.g., Sentinel-2A and B images), or even Google Earth images (Singh et al, 2020;Yariyan et al, 2020;Hafner et al, 2021). Terrain parameters like slope gradient and curvature have also been added to the avalanche detection process using Digital Elevation Models (DEM) combined with Landsat-8 images (Bühler et al, 2018;Singh et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, we used optical data to detect avalanches on a long-term basis and built an open-access script in Google Engine interface: Snow Avalanche Frequency Estimation (SAFE). Landsat-5, 7 and 8 products were used as their resolution (30 m, i.e., minimum detectible size of 900 m²) is sufficient to detect larger avalanches (Abermann et al, 2019;Eckerstorfer et al, , 2014Hafner et al, 2021;Singh et al, 2019Singh et al, , 2020Smith et al, 2020;Yariyan et al, 2020). Our objective is to automatically map annual avalanche occurrence over the past 32 years using Landast-5, 7 and 8 image archives in the Amu Panj basin of Afghanistan.…”

Section: Introductionmentioning

confidence: 99%

Snow Avalanche Frequency Estimation (SAFE): 32 years of remote hazard monitoring in Afghanistan

Caiserman¹,

Sidle²,

Gurung

2022

Preprint

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Abstract. Snow avalanches are the predominant hazards in winter in high elevation mountains. They cause damage to both humans and assets but cannot be accurately predicted. Until now, only local maps to estimate snow avalanche risk have been produced. Here we show how remote sensing can accurately inventory large avalanches every year at a basin scale using a 32-yr snow index derived from Landsat satellite archives. This Snow Avalanche Frequency Estimation (SAFE) built in an open-access Google Engine script maps snow hazard frequency and targets vulnerable areas in remote regions of Afghanistan, one of the most data-limited areas worldwide. SAFE correctly detected of the actual avalanches identified on Google Earth and in the field (Probability of Detection 0.77 and Positive Predictive Value 0.96). A total of 810,000 large avalanches occurred since 1990 within an area of 28,500 km2 with a mean frequency of 0.88 avalanches/km2yr−1, damaging villages and blocking roads and streams. Snow avalanche frequency did not significantly change with time, but a northeast shift of these hazards was evident. SAFE is the first robust model that can be used worldwide and is capable of filling data voids on snow avalanche impacts in inaccessible regions.

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GIS-based spatial modeling of snow avalanches using four novel ensemble models

Cited by 59 publications

References 65 publications

Flood susceptibility mapping using an improved analytic network process with statistical models

Flood susceptibility mapping using an improved analytic network process with statistical models

Modeling the supply, demand, and stress of water resources using ecosystem services concept in Sirvan River Basin (Kurdistan-Iran)

Snow Avalanche Frequency Estimation (SAFE): 32 years of remote hazard monitoring in Afghanistan

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