Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping
Snow avalanche as a natural disaster severely affects socio-economic and geomorphic processes through damaging ecosystems, vegetation, landscape, infrastructures, transportation networks, and human life. Modeling the snow avalanche has been seen as an essential approach for understanding the mountai...
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doaj-9125ba53f9d74d3fb2d809f97bfc13b02021-03-30T04:01:20ZengIEEEIEEE Access2169-35362020-01-01814596814598310.1109/ACCESS.2020.30148169160950Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility MappingAmirhosein Mosavi0https://orcid.org/0000-0003-4842-0613Ataollah Shirzadi1Bahram Choubin2https://orcid.org/0000-0001-6350-7157Fereshteh Taromideh3https://orcid.org/0000-0003-0966-3559Farzaneh Sajedi Hosseini4https://orcid.org/0000-0003-3357-7140Moslem Borji5Himan Shahabi6https://orcid.org/0000-0001-5091-6947Aryan Salvati7https://orcid.org/0000-0002-6063-7893Adrienn A. Dineva8https://orcid.org/0000-0001-8043-7023Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, VietnamDepartment of Rangeland and Watershed Management, College of Natural Resources, University of Kurdistan, Sanandaj, IranSoil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, IranDepartment of Irrigation, Sari Agricultural Sciences and Natural Resources University, Sari, IranReclamation of Arid and Mountainous Regions Department, Faculty of Natural Resources, University of Tehran, Karaj, IranReclamation of Arid and Mountainous Regions Department, Faculty of Natural Resources, University of Tehran, Karaj, IranDepartment of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, IranReclamation of Arid and Mountainous Regions Department, Faculty of Natural Resources, University of Tehran, Karaj, IranInstitute of Research and Development, Duy Tan University, Da Nang, VietnamSnow avalanche as a natural disaster severely affects socio-economic and geomorphic processes through damaging ecosystems, vegetation, landscape, infrastructures, transportation networks, and human life. Modeling the snow avalanche has been seen as an essential approach for understanding the mountainous landscape dynamics to assess hazard susceptibility leading to effective mitigation and resilience. Therefore, the main aim of this study is to introduce and implement an ensemble machine learning model of random subspace (RS) based on a classifier, functional tree (FT), named RSFT model for snow avalanche susceptibility mapping at Karaj Watershed, Iran. According to the best knowledge of literature, the proposed model, RSFT, has not earlier been introduced and implemented for snow avalanche modeling and mapping over the world. Four benchmark models, including logistic regression (LR), logistic model tree (LMT), alternating decision tree (ADT), and functional trees (FT) models were used to check the goodness-of-fit and prediction accuracy of the proposed model. To achieve this objective, the most important factors among many climatic, topographic, lithologic, and hydrologic factors, which affect the snow accumulation and snow avalanche occurrence, were determined by the information gain ratio (IGR) technique. The goodness-of-fit and prediction accuracy of the models were evaluated by some statistical-based indexes including, sensitivity, specificity, accuracy, kappa, and area under the ROC curve, Friedman and Wilcoxon sign rank tests. Results indicated that the ensemble proposed model (RSFT), had the highest performance (Sensitivity = 94.1%, Specificity = 92.4%, Accuracy = 93.3%, and Kappa = 0.782) rather than the other soft-computing benchmark models. The snow avalanche susceptibility maps indicated that the high and very high susceptibility avalanche areas are located in the north and northeast parts of the study area, which have a higher elevation with more precipitation and lower temperatures.https://ieeexplore.ieee.org/document/9160950/Snow avalanchesusceptibility mappingensemble approachfeature selection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Amirhosein Mosavi Ataollah Shirzadi Bahram Choubin Fereshteh Taromideh Farzaneh Sajedi Hosseini Moslem Borji Himan Shahabi Aryan Salvati Adrienn A. Dineva |
spellingShingle |
Amirhosein Mosavi Ataollah Shirzadi Bahram Choubin Fereshteh Taromideh Farzaneh Sajedi Hosseini Moslem Borji Himan Shahabi Aryan Salvati Adrienn A. Dineva Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping IEEE Access Snow avalanche susceptibility mapping ensemble approach feature selection |
author_facet |
Amirhosein Mosavi Ataollah Shirzadi Bahram Choubin Fereshteh Taromideh Farzaneh Sajedi Hosseini Moslem Borji Himan Shahabi Aryan Salvati Adrienn A. Dineva |
author_sort |
Amirhosein Mosavi |
title |
Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping |
title_short |
Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping |
title_full |
Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping |
title_fullStr |
Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping |
title_full_unstemmed |
Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping |
title_sort |
towards an ensemble machine learning model of random subspace based functional tree classifier for snow avalanche susceptibility mapping |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Snow avalanche as a natural disaster severely affects socio-economic and geomorphic processes through damaging ecosystems, vegetation, landscape, infrastructures, transportation networks, and human life. Modeling the snow avalanche has been seen as an essential approach for understanding the mountainous landscape dynamics to assess hazard susceptibility leading to effective mitigation and resilience. Therefore, the main aim of this study is to introduce and implement an ensemble machine learning model of random subspace (RS) based on a classifier, functional tree (FT), named RSFT model for snow avalanche susceptibility mapping at Karaj Watershed, Iran. According to the best knowledge of literature, the proposed model, RSFT, has not earlier been introduced and implemented for snow avalanche modeling and mapping over the world. Four benchmark models, including logistic regression (LR), logistic model tree (LMT), alternating decision tree (ADT), and functional trees (FT) models were used to check the goodness-of-fit and prediction accuracy of the proposed model. To achieve this objective, the most important factors among many climatic, topographic, lithologic, and hydrologic factors, which affect the snow accumulation and snow avalanche occurrence, were determined by the information gain ratio (IGR) technique. The goodness-of-fit and prediction accuracy of the models were evaluated by some statistical-based indexes including, sensitivity, specificity, accuracy, kappa, and area under the ROC curve, Friedman and Wilcoxon sign rank tests. Results indicated that the ensemble proposed model (RSFT), had the highest performance (Sensitivity = 94.1%, Specificity = 92.4%, Accuracy = 93.3%, and Kappa = 0.782) rather than the other soft-computing benchmark models. The snow avalanche susceptibility maps indicated that the high and very high susceptibility avalanche areas are located in the north and northeast parts of the study area, which have a higher elevation with more precipitation and lower temperatures. |
topic |
Snow avalanche susceptibility mapping ensemble approach feature selection |
url |
https://ieeexplore.ieee.org/document/9160950/ |
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