Next Article in Journal
Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data
Next Article in Special Issue
A GIS-Based Water Balance Approach Using a LiDAR-Derived DEM Captures Fine-Scale Vegetation Patterns
Previous Article in Journal
Editorial for Special Issue: “Remotely Sensed Albedo”
Previous Article in Special Issue
Determining Optimal Solar Power Plant Locations Based on Remote Sensing and GIS Methods: A Case Study from Croatia
Open AccessArticle

Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh 758307, Viet Nam
Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh 758307, Viet Nam
Soil Conservation and Water Management Research Department, Chaharmahal and Bakhtiari Agricultural and Natural Resources Research and Education Center, AREEO, Shahrekord 8814843114, Iran
Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, SE-106 91 Stockholm, Sweden
Department of Geography, University of Tartu, Vanemuise St. 46, 51003 Tartu, Estonia
NIWA, Gate 10 Silverdale Road, Hillcrest, Hamilton 3216, New Zealand
Faculty of Natural Resources, University of Tehran, Karaj 31587-77871, Iran
Soil Physics and Land Management Group, Wageningen University, Droevendaalsesteeg 4, 6708 PB Wageningen, The Netherlands
Center for Agricultural Research and Ecological Studies (CARES), Vietnam National University of Agriculture (VNUA), Trau Quy, Gia Lam, Hanoi 100000, Vietnam
Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1943;
Received: 11 July 2019 / Revised: 9 August 2019 / Accepted: 14 August 2019 / Published: 20 August 2019
Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models—support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards. View Full-Text
Keywords: natural disasters; Sentinel-1; hazard; artificial intelligence; Asara watershed natural disasters; Sentinel-1; hazard; artificial intelligence; Asara watershed
Show Figures

Graphical abstract

MDPI and ACS Style

Rahmati, O.; Yousefi, S.; Kalantari, Z.; Uuemaa, E.; Teimurian, T.; Keesstra, S.; Pham, T.D.; Tien Bui, D. Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran. Remote Sens. 2019, 11, 1943.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop