Machine Learning-based Hazard Classification
Machine Learning-Based Hazard Classification is a methodological framework within hazard classification that employs machine learning algorithms to automatically identify, categorize, and prioritize hazards by analyzing patterns in data, distinguishing itself from traditional rule-based or expert-driven approaches through its reliance on data-driven model training and predictive analytics.
72
Publications
3.8K
Citations
264
Authors
141
Institutions
Publications per year
2017–2026
66
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141
China University of Mining and Technology
Xuzhou, China
3 h-index
14 publications
953 citations
Swiss Federal Institute of Aquatic Science and Technology
Dübendorf, Switzerland
2 h-index
6 publications
342 citations
North Dakota State University
Fargo, United States
2 h-index
5 publications
121 citations

University of Alberta
Edmonton, Canada
2 h-index
8 publications
152 citations
University of Technology Sydney
Sydney, Australia
2 h-index
4 publications
421 citations

Huazhong University of Science and Technology
Wuhan, China
2 h-index
5 publications
627 citations

Ton Duc Thang University
Ho Chi Minh City, Vietnam
2 h-index
2 publications
238 citations
Soil Conservation and Watershed Management Research
Tehran, Iran
2 h-index
2 publications
238 citations
1–8 of 141
6 publications · 514 citations
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4 publications · 244 citations
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3 publications · 83 citations
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2 publications · 253 citations
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2 publications · 138 citations
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