Spatial modeling of radon potential mapping using deep learning algorithms

Geocarto International, Год журнала: 2021, Номер 37(25), С. 9560 - 9582

Опубликована: Дек. 25, 2021

Radon potential mapping is challenging due to the limited availability of information. In this study, a new modeling process using deep learning models based on convolution neural network (CNN), long short-term memory (LSTM), and recurrent (RNN) presented predict radon in northwestern part Gangwon Province, South Korea. The used data study are two sets dependent variables (measured soil gas concentrations) independent (radon conditioning factors: lithology; distance from lineament; mean calcium oxide [Cao], potassium [K2O], ferric [Fe2O3] concentrations; effective depth; topsoil texture; drainage). were validated area under receiver operating curve (AUC), squared error (MSE), root square (RMSE), standard deviation (StD). CNN model with AUC values 0.906 0.905 testing stages, respectively, introduced as optimal model. lowest StD, MSE, RMSE CNN, LSTM, RNN models, respectively. Our results show that use generate maps promising reliable.

Язык: Английский

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Snow Avalanche Hazard Mapping Using a GIS-Based AHP Approach: A Case of Glaciers in Northern Pakistan from 2012 to 2022

Remote Sensing, Год журнала: 2023, Номер 15(22), С. 5375 - 5375

Опубликована: Ноя. 16, 2023

Snow avalanches are a type of serious natural disaster that commonly occur in snow-covered mountains with steep terrain characteristics. Susceptibility analysis is pressing issue today and helps decision makers to implement appropriate avalanche risk reduction strategies. Avalanche susceptibility maps provide preliminary method for evaluating places likely be vulnerable stop or reduce the risks such disasters. The current study aims identify areas (ranging from extremely high low danger) by considering geo-morphological geological variables employing an Analytical Hierarchy Approach (AHP) GIS platform potential snow zones Karakoram region Northern Pakistan. Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) was used extract elevation, slope, aspect, roughness, curvature area. This includes identification variable land cover (LC), which obtained Landsat 8 Operational Land Imager (OLI) satellite. result showed approach established this provided quick reliable tool map area, it might also work other glacier sites parts world assessments.

Язык: Английский

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Universal Snow Avalanche Modeling Index Based on SAFI–Flow-R Approach in Poorly-Gauged Regions

ISPRS International Journal of Geo-Information, Год журнала: 2024, Номер 13(9), С. 315 - 315

Опубликована: Сен. 1, 2024

Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step research collection of data field and their processing geographic information systems remote sensing. period 2023–2024, were mapped field, later, as points (GIS) overlapped with dominant natural conditions study area. second involves main criteria (snow cover, terrain slope, land use) evaluating values obtain Formation (SAFI). Thresholds obtained through formation inventory used develop SAFI index. index applied aim identifying locations (source areas). calculation include Normalized Difference (NDSI > 0.6), slope (20–60°) use (pastures, meadows). third presents analysis meteorological (winter precipitation air temperature). fourth modeling propagation (simulation) other parts Flow-R software 2.0. results show that 282.9 km2 area (Šar Mountains, Serbia) avalanches, thickness potentially triggered layer being 50 cm. With 5 m thick snowpack, 299.9 would be susceptible. validation using ROC-AUC method confirms very high predictive power (0.94). SAFI–Flow-R approach offers which no available, representing an advance areas where historical do not exist. can planning, zoning vulnerable areas, adopting adequate environmental protection measures.

Язык: Английский

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Identification and Assessment of Avalanche Hazards in Aerxiangou Section of Duku Expressway in TianShan Mountainous Region Based on Unmanned Aerial Vehicle Photography

Research in Cold and Arid Regions, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Distribution, Trends, and Drivers of Snow Avalanche Susceptibility in the Earth's Third Pole

Опубликована: Янв. 1, 2025

Язык: Английский

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