About this Research Topic
Natural and engineering disasters, which include landslides, rockfall, rainstorms, dam failure, floods, earthquakes, road and building disasters and wildfires, appear as results of the progressive or extreme evolution of climatic, tectonic and geomorphological processes and human engineering activities. It is significant to explore the failure mechanism and carry out spatial modeling of these engineering and natural disasters due to their serious harm to the safety of people's lives and property.
The data-based methods, including advanced and successful remote sensing, geographic information systems, machine learning and numerical simulation technical methods, are promising tools to analyze these complex disasters. Machine Learning models such as neuro-fuzzy logic, decision tree, artificial neural network, deep learning and evolutionary algorithms are characterized by their abilities to produce knowledge and discover hidden and unknown patterns and trends from large databases, whereas remote sensing and geographic information systems appear as significant technology equipped with tools for data manipulation and advanced mathematical modeling. What is more, numerical simulation can also be acknowledged as advanced technologies for discovering hidden failure mechanisms of disasters.
This Research Topic aims to provide an outlet for peer-reviewed publications that implement state-of-the-art data-based methods and techniques incorporating machine learning and/or numerical simulation techniques to analyze, map, monitor, and assess various natural and engineering disasters. The topics include, without being limited to, the following areas:
• Failure mechanism and affecting factors exploration of landslides, tailing dam, rockfall, debris flow, mountainous flood, earthquake, tunnel collapse, cracking in soils/rocks, et al;
• Vulnerability assessment of various hazard-affected bodies, as well as loss and damages evaluation after natural and engineering disasters;
• Susceptibility, hazard and risk prediction and mapping of regional and/or single disasters;
• Monitoring, spatial-temporal prediction modeling and early warning of various disasters using advanced earth observation technologies; and
• Tracking the frontiers of machine learning and numerical simulation methods for more efficient modeling of natural and engineering disasters.
The data-based methods, including advanced and successful remote sensing, geographic information systems, machine learning and numerical simulation technical methods, are promising tools to analyze these complex disasters. Machine Learning models such as neuro-fuzzy logic, decision tree, artificial neural network, deep learning and evolutionary algorithms are characterized by their abilities to produce knowledge and discover hidden and unknown patterns and trends from large databases, whereas remote sensing and geographic information systems appear as significant technology equipped with tools for data manipulation and advanced mathematical modeling. What is more, numerical simulation can also be acknowledged as advanced technologies for discovering hidden failure mechanisms of disasters.
This Research Topic aims to provide an outlet for peer-reviewed publications that implement state-of-the-art data-based methods and techniques incorporating machine learning and/or numerical simulation techniques to analyze, map, monitor, and assess various natural and engineering disasters. The topics include, without being limited to, the following areas:
• Failure mechanism and affecting factors exploration of landslides, tailing dam, rockfall, debris flow, mountainous flood, earthquake, tunnel collapse, cracking in soils/rocks, et al;
• Vulnerability assessment of various hazard-affected bodies, as well as loss and damages evaluation after natural and engineering disasters;
• Susceptibility, hazard and risk prediction and mapping of regional and/or single disasters;
• Monitoring, spatial-temporal prediction modeling and early warning of various disasters using advanced earth observation technologies; and
• Tracking the frontiers of machine learning and numerical simulation methods for more efficient modeling of natural and engineering disasters.
Keywords: natural disasters, engineering disasters, susceptibility, hazardous and risk mapping, data-based models, machine learning, earth observation data
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