ArticleScientific reports2020
A machine learning framework for multi-hazards modeling and mapping in a mountainous area.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Distinguishing compound and cumulative hazards using machine learning and fuzzy logic in multi-hazard susceptibility mapping.Scientific reports · 2026Article
- Global vulnerability assessment of mobile telecommunications infrastructure to climate hazards using crowdsourced open data.Nature communications · 2026Article
- An integrated multi-hazard assessment using machine learning in the complex terrains of Northern Pakistan.Scientific reports · 2026Article
- Integrating geospatial intelligence and machine learning for flood susceptibility mapping.Scientific reports · 2026Article
- Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires.Scientific reports · 2025Article
- Article
- Geological Hazard Susceptibility Analysis and Developmental Characteristics Based on Slope Unit, Using the Xinxian County, Henan Province as an Example.Sensors (Basel, Switzerland) · 2024Article
- Multi-hazard exposure mapping under climate crisis using random forest algorithm for the Kalimantan Islands, Indonesia.Scientific reports · 2023Article
- Machine learning-based assessment of storm surge in the New York metropolitan area.Scientific reports · 2022Article
- Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models.Sensors (Basel, Switzerland) · 2021Article
- Mountain surface processes and regulation.Scientific reports · 2021Article
- Assessing the susceptibility of schools to flood events in Iran.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study sought to produce an accurate multi-hazard risk map for a mountainous region of Iran. The study area is in southwestern Iran. The region has experienced numerous extreme natural events in recent decades. This study models the probabilities of snow avalanches, landslides, wildfires, land subsidence, and floods using machine learning models that include support vector machine (SVM), boosted regression tree (BRT), and generalized linear model (GLM). Climatic, topographic, geological, social, and morphological factors were the main input variables used. The data were obtained from several sources. The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is the most accurate for predicting landslides, land subsidence, and flood hazards in the study area. GLM is the best algorithm for wildfire mapping, and FDA is the most accurate model for predicting snow avalanche risk. The values of AUC (area under curve) for all five hazards using the best models are greater than 0.8, demonstrating that the model's predictive abilities are acceptable. A machine learning approach can prove to be very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling. The predictive maps produce valuable baselines for risk management in the study area, providing evidence to manage future human interaction with hazards.
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Registered trials
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