Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: closed (10 January 2023) | Viewed by 38926
Special Issue Editors
Interests: natural hazards; water resources; engineering geology; GIS; machine learning; soft computing; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: hydrogeology; environmental impact assessment; natural hazard susceptibility; spatial modeling; machine learning; geology; civil engineering
Special Issues, Collections and Topics in MDPI journals
Interests: natural hazards; environmental geology; engineering geology; GIS; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: natural hazards; water resources; machine learning; soft computing; GIS
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As it well established natural hazards in most cases are responsible for severe financial and human losses across the world. Natural hazards, which involve earthquakes, floods, landslides, volcanic eruptions, wildfires, droughts and soil erosion and degradation, are the result of progressive or extreme changes in climatic, tectonic and geo-morphological processes but also the impact of human activities on the geo-environment. Their complex nature, variation in frequency, speed, duration and area affected are some of the characteristics that are responsible for not fully understanding the mechanism behind their evolution and extent of occurrence. The main efforts of scientists from various geophysical disciplines, is to create conceptual models, develop intelligent computing techniques, machine learning (ML) algorithms, apply remote sensing (RS) technology within a geographic information system (GIS) framework that captures their complex nature and provide accurate prediction concerning their spatial and temporal occurrence. ML algorithms provide a “recipe” to computers for how to learn from existing data, produce knowledge and discover hidden and unknown patterns and trends from large databases, whereas GIS appears as a significant technology equipped with tools for data manipulation and advanced modeling. In recent years, ML, which includes algorithms and methods that are based on the concept of fuzzy and neuro-fuzzy logic, decision tree models, artificial neural networks, deep learning and evolutionary algorithms, along with GIS and RS technology, have been proposed as alternative investigation tools for natural risk phenomena, susceptibility and hazardous mapping.
This Special Issue aims to provide an outlet for peer-reviewed publications that implement state-of-the-art methods and techniques incorporating RS technology, ML methods and GIS so as to map, monitor, evaluate and assess natural hazards.
Potential topics of interest include (but are not limited to) regional or global case studies concerning Natural Risk Phenomena Prediction and Assessment, software development and the implementation of machine learning, optimization, deep learning techniques and meta-heuristic algorithms. Specifically, this Special Issue aims to cover, but is not limited to, the following areas:
- Monitoring, mapping and assessing earthquakes, landslides, floods, wildfires and soil erosion;
- Evaluating the loss and damages after earthquakes, floods, landslides, wildfires and soil erosion.
Dr. Paraskevas Tsangaratos
Dr. Wei Chen
Dr. Ioanna Ilia
Dr. Haoyuan Hong
Guest Editors
Manuscript Submission Information
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Keywords
- earth observation data – remote sensing technology
- geographic information systems
- machine learning, soft computing
- landslide susceptibility, hazardous and risk mapping
- flood susceptibility mapping and disaster management
- wildfire susceptibility mapping
- soil erosion/degradation
- earthquakes/tsunamis
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