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Identification the areas prone to gully erosion and landslides in the form of two-hazards map using machine learning models in Gorganrood watershed
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Narges Javidan   , Ataollah Kavian * , Sajad Rajabi , Hamid Reza Pourghasemi , Zeinab Jafarian |
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Abstract: (803 Views) |
The aim of this study is to identify the areas prone to gully erosion and landslide as a two-hazard map in the form of a single map in Gorganrood watershed. In addition, the use of two machine learning models such as RF and SVM to establishing the spatial relationship between these hazards and the GEFs and any hazard susceptibility mapping separately. In addition, the validation of the hazards susceptibility maps was conducted based on the ROC curve method, and the best model was chosen with the highest predictive performances. Finally, by combining the susceptibility landslides and gully maps, the two-hazard probability maps were produced, which were a combination of different models and the best model. The results showed a RF model with (AUC = 82.9) for landslide and (AUC = 96.9) for gully erosion have higher accuracy compared to the SVM model with a value of (AUC = 0.76) for landslide and (AUC = 93.9) for gully erosion. Finally, a single and comprehensive map was obtained by combining of the each hazard susceptibility map for identification the areas prone to both hazards based on both models. The final two-hazard map can be used as a valuable tool for sustainable land use planning in multi-hazard prone areas. |
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| Keywords: Spatial prediction, Natural hazards, two- hazard map, Machine learning models, Relative receiver operating characteristic curve |
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Full-Text [PDF 1731 kb]
(600 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2021/10/26 | Accepted: 2022/04/20 | Published: 2023/12/19
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