About this Research Topic
This Research Topic is Volume IV of a series. The previous volumes can be found here:
Spatial Modelling and Failure Analysis of Natural and Engineering Disasters through Data-based Methods
Spatial Modelling and Failure Analysis of Natural and Engineering Disasters through Data-based Methods - Volume II
Spatial Modelling and Failure Analysis of Natural and Engineering Disasters through Data-Based Methods: Volume III
Natural disasters, which include landslides, rock falls, rainstorms, floods, and earthquakes, 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 natural disasters due to their serious harm to the safety of people's lives and property.
Various advanced methods, including successful remote sensing, geographic information systems, machine learning models, and numerical simulation techniques, are promising tools to analyze these complex disasters. Machine Learning models such as neuro-fuzzy logic, decision trees, artificial neural networks, 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 an advanced technology for discovering hidden failure mechanisms 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 mechanisms, spatial and time series prediction, susceptibility mapping, hazard assessment, vulnerability modeling, risk assessment and early warning of complex natural disasters.
This Research Topic aims to provide an outlet for peer-reviewed publications that implement state-of-the-art methods and techniques incorporating machine learning and/or numerical simulation techniques to analyze, map, monitor, and assess various natural disasters. This Research Topic aims to cover, without being limited to, the following areas:
• Failure mechanism and affecting factors exploration of landslides, rock fall, debris flow, mountainous flood, earthquake, et al.
• Vulnerability assessment of various hazard-affected bodies, as well as loss and damages evaluation after natural 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.
• Tracking the frontiers of machine learning and numerical simulation methods for more efficient modeling of natural disasters.
Spatial Modelling and Failure Analysis of Natural and Engineering Disasters through Data-based Methods
Spatial Modelling and Failure Analysis of Natural and Engineering Disasters through Data-based Methods - Volume II
Spatial Modelling and Failure Analysis of Natural and Engineering Disasters through Data-Based Methods: Volume III
Natural disasters, which include landslides, rock falls, rainstorms, floods, and earthquakes, 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 natural disasters due to their serious harm to the safety of people's lives and property.
Various advanced methods, including successful remote sensing, geographic information systems, machine learning models, and numerical simulation techniques, are promising tools to analyze these complex disasters. Machine Learning models such as neuro-fuzzy logic, decision trees, artificial neural networks, 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 an advanced technology for discovering hidden failure mechanisms 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 mechanisms, spatial and time series prediction, susceptibility mapping, hazard assessment, vulnerability modeling, risk assessment and early warning of complex natural disasters.
This Research Topic aims to provide an outlet for peer-reviewed publications that implement state-of-the-art methods and techniques incorporating machine learning and/or numerical simulation techniques to analyze, map, monitor, and assess various natural disasters. This Research Topic aims to cover, without being limited to, the following areas:
• Failure mechanism and affecting factors exploration of landslides, rock fall, debris flow, mountainous flood, earthquake, et al.
• Vulnerability assessment of various hazard-affected bodies, as well as loss and damages evaluation after natural 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.
• Tracking the frontiers of machine learning and numerical simulation methods for more efficient modeling of natural disasters.
Keywords: Natural Disasters, Susceptibility Hazardous and Risk Mapping, Machine Learning, Numerical Simulation, 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.
