Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability
Abstract The regional multi-hazards risk assessment poses difficulties due to data access challenges, and the potential interactions between multi-hazards and social vulnerability. For better natural hazards risk perception and preparedness, it is important to study the nature-hazards risk distribut...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-40159-9 |
| _version_ | 1797452961791606784 |
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| author | Tianjie Zhang Donglei Wang Yang Lu |
| author_facet | Tianjie Zhang Donglei Wang Yang Lu |
| author_sort | Tianjie Zhang |
| collection | DOAJ |
| description | Abstract The regional multi-hazards risk assessment poses difficulties due to data access challenges, and the potential interactions between multi-hazards and social vulnerability. For better natural hazards risk perception and preparedness, it is important to study the nature-hazards risk distribution in different areas, specifically a major priority in the areas of high hazards level and social vulnerability. We propose a multi-hazards risk assessment method which considers social vulnerability into the analyzing and utilize machine learning-enabled models to solve this issue. The proposed methodology integrates three aspects as follows: (1) characterization and mapping of multi-hazards (Flooding, Wildfires, and Seismic) using five machine learning methods including Naïve Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), and K-Means (KM); (2) evaluation of social vulnerability with a composite index tailored for the case-study area and using machine learning models for classification; (3) risk-based quantification of spatial interaction mechanisms between multi-hazards and social vulnerability. The results indicate that RF model performs best in both hazard-related and social vulnerability datasets. The most cities at multi-hazards risk account for 34.12% of total studied cities (covering 20.80% land). Additionally, high multi-hazards level and socially vulnerable cities account for 15.88% (covering 4.92% land). This study generates a multi-hazards risk map which show a wide variety of spatial patterns and a corresponding understanding of where regional high hazards potential and vulnerable areas are. It emphasizes an urgent need to implement information-based prioritization when natural hazards coming, and effective policy measures for reducing natural-hazards risks in future. |
| first_indexed | 2024-03-09T15:16:25Z |
| format | Article |
| id | doaj.art-432e61a13e424453b4978c177cbfd6c0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:16:25Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-432e61a13e424453b4978c177cbfd6c02023-11-26T13:04:31ZengNature PortfolioScientific Reports2045-23222023-08-0113111410.1038/s41598-023-40159-9Machine learning-enabled regional multi-hazards risk assessment considering social vulnerabilityTianjie Zhang0Donglei Wang1Yang Lu2Environmental Research Building, Department of Computer Science, Boise State UniversityEnvironmental Research Building, Department of Civil Engineering, Boise State UniversityBoise State UniversityAbstract The regional multi-hazards risk assessment poses difficulties due to data access challenges, and the potential interactions between multi-hazards and social vulnerability. For better natural hazards risk perception and preparedness, it is important to study the nature-hazards risk distribution in different areas, specifically a major priority in the areas of high hazards level and social vulnerability. We propose a multi-hazards risk assessment method which considers social vulnerability into the analyzing and utilize machine learning-enabled models to solve this issue. The proposed methodology integrates three aspects as follows: (1) characterization and mapping of multi-hazards (Flooding, Wildfires, and Seismic) using five machine learning methods including Naïve Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), and K-Means (KM); (2) evaluation of social vulnerability with a composite index tailored for the case-study area and using machine learning models for classification; (3) risk-based quantification of spatial interaction mechanisms between multi-hazards and social vulnerability. The results indicate that RF model performs best in both hazard-related and social vulnerability datasets. The most cities at multi-hazards risk account for 34.12% of total studied cities (covering 20.80% land). Additionally, high multi-hazards level and socially vulnerable cities account for 15.88% (covering 4.92% land). This study generates a multi-hazards risk map which show a wide variety of spatial patterns and a corresponding understanding of where regional high hazards potential and vulnerable areas are. It emphasizes an urgent need to implement information-based prioritization when natural hazards coming, and effective policy measures for reducing natural-hazards risks in future.https://doi.org/10.1038/s41598-023-40159-9 |
| spellingShingle | Tianjie Zhang Donglei Wang Yang Lu Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability Scientific Reports |
| title | Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability |
| title_full | Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability |
| title_fullStr | Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability |
| title_full_unstemmed | Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability |
| title_short | Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability |
| title_sort | machine learning enabled regional multi hazards risk assessment considering social vulnerability |
| url | https://doi.org/10.1038/s41598-023-40159-9 |
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