About this Research Topic
This Research Topic is Volume III of a series. The previous volume can be found here: Spatial Modelling and Failure Analysis of Natural and Engineering Disasters through Data-based Methods - Volume II and Spatial Modelling and Failure Analysis of Natural and Engineering Disasters through Data-based Methods
Natural and engineering disasters, which include landslides, rock fall, rainstorm, 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 techniques methods, are promising tools to analyze these complex disasters. Machine Learning models such as neurofuzzy 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, the numerical simulation can also be acknowledged as advanced technologies for discovering hidden failure mechanism of disasters. The main objective of this Research Topic is to provide a scientific forum for advancing the successful implementation of Machine Learning (ML) and numerical simulation techniques in operation rules, failure mechanism, spatial and time series prediction, susceptibility mapping, hazard assessment, vulnerability modeling, risk assessment and early warning of complex natural and engineering 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. This Research Topic aims to cover, without being limited to, the following areas:
• Failure mechanism and affecting factors exploration of landslides, tailing dam, rock fall, debris flow, mountainous flood, earthquake, tunnel collapse and cracking in soils/rocks
• Vulnerability assessment of various hazard-affected body, 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 modelling, and early warning of various disasters using advanced earth observation technologies
• Tracking the frontiers of machine learning and numerical simulation methods for more efficient modelling of natural and engineering disasters
Natural and engineering disasters, which include landslides, rock fall, rainstorm, 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 techniques methods, are promising tools to analyze these complex disasters. Machine Learning models such as neurofuzzy 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, the numerical simulation can also be acknowledged as advanced technologies for discovering hidden failure mechanism of disasters. The main objective of this Research Topic is to provide a scientific forum for advancing the successful implementation of Machine Learning (ML) and numerical simulation techniques in operation rules, failure mechanism, spatial and time series prediction, susceptibility mapping, hazard assessment, vulnerability modeling, risk assessment and early warning of complex natural and engineering 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. This Research Topic aims to cover, without being limited to, the following areas:
• Failure mechanism and affecting factors exploration of landslides, tailing dam, rock fall, debris flow, mountainous flood, earthquake, tunnel collapse and cracking in soils/rocks
• Vulnerability assessment of various hazard-affected body, 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 modelling, and early warning of various disasters using advanced earth observation technologies
• Tracking the frontiers of machine learning and numerical simulation methods for more efficient modelling of natural and engineering disasters
Keywords: natural disasters, engineering disasters, susceptibility, hazardous and risk mapping, data-based models, machine learning, earth observation data
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
