Evaluating novel hybrid models based on GIS for snow avalanche susceptibility mapping: A comparative study

2021 ◽  
pp. 103453
Author(s):  
Peyman Yariyan ◽  
Ebrahim Omidvar ◽  
Mohammadreza Karami ◽  
Artemi Cerdà ◽  
Quoc Bao Pham ◽  
...  
Keyword(s):  
Comparative Study ◽  
Hybrid Models ◽  
Susceptibility Mapping ◽  
Snow Avalanche

A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping

CATENA ◽  
2018 ◽  
Vol 166 ◽  
pp. 317-327 ◽  
Author(s):  
A-Xing Zhu ◽  
Yamin Miao ◽  
Rongxun Wang ◽  
Tongxin Zhu ◽  
Yongcui Deng ◽  
...  
Keyword(s):  
Comparative Study ◽  
Landslide Susceptibility ◽  
Expert Knowledge ◽  
Susceptibility Mapping ◽  
Data Driven ◽  
Landslide Susceptibility Mapping ◽  
Knowledge Based

Digital Rock-Fall and Snow Avalanche Susceptibility Mapping of Norway

2013 ◽  
pp. 313-319 ◽  
Author(s):  
Jan Høst ◽  
Marc-Henri Derron ◽  
Kari Sletten
Keyword(s):  
Susceptibility Mapping ◽  
Rock Fall ◽  
Snow Avalanche ◽  
Digital Rock

scholarly journals Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145968-145983 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Ataollah Shirzadi ◽  
Bahram Choubin ◽  
Fereshteh Taromideh ◽  
Farzaneh Sajedi Hosseini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Susceptibility Mapping ◽  
Snow Avalanche ◽  
Random Subspace ◽  
Ensemble Machine Learning ◽  
Machine Learning Model ◽  
Tree Classifier

A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran)

2020 ◽  
Vol 79 (10) ◽  
pp. 5291-5308
Author(s):  
Sayed Naeim Emami ◽  
Saleh Yousefi ◽  
Hamid Reza Pourghasemi ◽  
Shahla Tavangar ◽  
M. Santosh
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Mass Movement ◽  
Susceptibility Mapping

scholarly journals GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

2020 ◽  
Vol 10 (6) ◽  
pp. 2039 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Saeid Janizadeh ◽  
Mohammadtaghi Avand ◽  
Wei Chen ◽  
Mohsen Farzin ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Characteristic Curve ◽  
Absolute Error ◽  
Hybrid Models ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Aerial Photographs ◽  
River Channels ◽  
Grazing Land ◽  
Semi Arid

Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision–recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran.

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