A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran
Bahareh Ghasemian1, Himan Shahabi1, Ataollah Shirzadi2
1Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran.
Sensors (Basel, Switzerland)
|February 26, 2022
Summary
A novel deep-learning model accurately mapped landslide susceptibility in Kamyaran, Iran. Key factors identified include distance to roads, road density, lithology, and land use, improving hazard assessment.
Area of Science:
- Geosciences
- Environmental Science
- Data Science
Background:
- Landslides pose significant risks in mountainous regions like Kurdistan Province, Iran.
- Accurate landslide susceptibility mapping is crucial for effective land-use planning and disaster mitigation.
Purpose of the Study:
- To develop and validate a robust deep-learning model for landslide susceptibility mapping in Kamyaran city.
- To identify the most influential conditioning factors for landslide occurrence.
Main Methods:
- A deep-learning (DP) model integrating ELM, DBN, BP, and GA was employed.
- 118 landslide locations were used for training and testing, with 25 conditioning factors analyzed.
- Information Gain Ratio (IGR) identified the top 16 significant factors, including distance to road, road density, lithology, and land use.
Main Results:
- The DP model achieved the highest accuracy (0.926) compared to SVM, REPTree, and NBTree.
- The model demonstrated strong performance with an Area Under the Curve (AUC) of 0.870.
- Distance to road, road density, lithology, and land use were identified as the most critical factors.
Conclusions:
- The proposed deep-learning model is a suitable and effective tool for landslide susceptibility mapping.
- The findings provide valuable insights for hazard assessment and risk management in landslide-prone areas.
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