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Open AccessArticle

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

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Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh 758307, Viet Nam
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Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh 758307, Viet Nam
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Soil Conservation and Water Management Research Department, Chaharmahal and Bakhtiari Agricultural and Natural Resources Research and Education Center, AREEO, Shahrekord 8814843114, Iran
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Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, SE-106 91 Stockholm, Sweden
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Department of Geography, University of Tartu, Vanemuise St. 46, 51003 Tartu, Estonia
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NIWA, Gate 10 Silverdale Road, Hillcrest, Hamilton 3216, New Zealand
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Faculty of Natural Resources, University of Tehran, Karaj 31587-77871, Iran
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Soil Physics and Land Management Group, Wageningen University, Droevendaalsesteeg 4, 6708 PB Wageningen, The Netherlands
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Center for Agricultural Research and Ecological Studies (CARES), Vietnam National University of Agriculture (VNUA), Trau Quy, Gia Lam, Hanoi 100000, Vietnam
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam
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Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1943; https://doi.org/10.3390/rs11161943
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
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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.

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