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Utilizing Advanced Spatial Analysis and Machine Learning Methods for Natural Hazard Assessments

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: closed (10 January 2024) | Viewed by 2489

Special Issue Editors

Dr. Ioanna Ilia

E-Mail Website
Guest Editor
Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15780 Athens, Greece
Interests: natural hazards; environmental geology; engineering geology; GIS; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Paraskevas Tsangaratos

E-Mail Website
Guest Editor
Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15780 Athens, Greece
Interests: natural hazards; water resources; engineering geology; GIS; machine learning; soft computing; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Chen

E-Mail Website
Guest Editor
College of Geology & Environment, Xi’an University of Science and Technology, Xi’an, China
Interests: hydrogeology; environmental impact assessment; natural hazard susceptibility; spatial modeling; machine learning; geology; civil engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Ioannis Matiatos

E-Mail Website
Guest Editor
Hellenic Centre for Marine Research, Institute of Marine Biological Resources and Inland Waters, Anavyssos, Greece
Interests: hydrogeology; water quality; GIS; isotope tracers; vulnerability; water resources; groundwater abstractions

Special Issue Information

Dear Colleagues,

Natural hazards, such as earthquakes, floods, landslides, land subsidence, volcanic eruptions, wildfires, droughts, soil erosion and water degradation, are responsible for severe financial and human losses around the world. They are mainly triggered by climatic, tectonic, and geo-morphological processes and human activities that have a negative impact on the geo-environment, including water resources. Despite the efforts of the scientific community to simulate highly accurate phenomena, some of the characteristics responsible for their evolution and severity are unknown. Natural hazards appear to have a complex nature, along with variations in frequency, speed, duration, and the area affected. These characteristics make it difficult to fully understand their occurrence.

In recent decades, technological developments have made it possible to simulate natural phenomena. In the field of geosciences, the enormous computing power of modern computing systems has enabled the development of sophisticated artificial intelligence and machine learning (ML) algorithms, the exploitation of "big data" using Remote Sensing (RS) technology and Geographic Information Systems (GIS), and the development of sophisticated techniques and methods of Spatial Analysis. The collaboration of these new technologies resulted in the development of innovative methodological approaches aimed at generating knowledge, discovering hidden and unknown patterns and trends, data manipulation, and advanced modeling tools.

Nowadays, algorithms and methods based on concepts of fuzzy and neuro-fuzzy logic, decision trees, artificial neural networks, deep learning and evolutionary algorithms, along with RS and GIS technology are the main tools for assessing natural hazards. 

This Special Issue aims to present peer reviewed publications that implement state-of-the-art methods and techniques incorporating Spatial Analysis, AI, and ML methods to map, monitor, evaluate, and assess natural hazards.

The thematic topics of the Special Issue are not limited to, but include regional or global case studies focused on software development and the implementation of artificial intelligence and machine learning, optimization, deep learning techniques, and meta-heuristic algorithms and also hazard and natural hazard risk assessments. Specifically, this includes the following areas:

  • Identifying, mapping, and monitoring earthquakes, floods, landslides, land subsidence, wildfires, soil erosion, and water degradation;
  • Evaluating the loss and damages to society and the environment after earthquakes, floods, landslides, land subsidence, wildfires, and soil erosion.

Dr. Ioanna Ilia
Dr. Paraskevas Tsangaratos
Dr. Wei Chen
Dr. Ioannis Matiatos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • earth observation data—remote sensing technology
  • geographic information systems
  • artificial intelligence, machine learning, and soft computing
  • landslide susceptibility, hazardous, and risk mapping
  • land subsidence mapping
  • flood susceptibility mapping and disaster management
  • wildfire susceptibility mapping
  • soil erosion/degradation
  • earthquakes/tsunamis
  • water resources degradation and protection.

Published Papers (2 papers)

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Research

17 pages, 2755 KiB  
Article
Evaluation of Tropical Cyclone Disaster Loss Using Machine Learning Algorithms with an eXplainable Artificial Intelligence Approach
by Shuxian Liu, Yang Liu, Zhigang Chu, Kun Yang, Guanlan Wang, Lisheng Zhang and Yuanda Zhang
Sustainability 2023, 15(16), 12261; https://0-doi-org.brum.beds.ac.uk/10.3390/su151612261 - 11 Aug 2023
Cited by 1 | Viewed by 946
Abstract
In the context of global warming, tropical cyclones (TCs) have garnered significant attention as one of the most severe natural disasters in China, particularly in terms of assessing the disaster losses. This study aims to evaluate the TC disaster loss (TCDL) using machine [...] Read more.
In the context of global warming, tropical cyclones (TCs) have garnered significant attention as one of the most severe natural disasters in China, particularly in terms of assessing the disaster losses. This study aims to evaluate the TC disaster loss (TCDL) using machine learning (ML) algorithms and identify the impact of specific feature factors on the prediction of model with an eXplainable Artificial Intelligence (XAI) approach, SHapley Additive exPlanations (SHAP). The results show that LightGBM outperforms Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) for estimating the TCDL grades, achieving the highest accuracy value of 0.86. According to the SHAP values, the three most important factors in the LightGBM classifier model are proportion of stations with rainfall exceeding 50 mm (ProRain), maximum wind speed (MaxWind), and maximum daily rainfall (MaxRain). Specifically, in the estimation of high TCDL grade, events characterized with MaxWind exceeding 30 m/s, MaxRain exceeding 200 mm, and ProRain exceeding 30% tend to exhibit a higher susceptibility to TC disaster due to positive SHAP values. This study offers a valuable tool for decision-makers to develop scientific strategies in the risk management of TC disaster. Full article
(This article belongs to the Special Issue Utilizing Advanced Spatial Analysis and Machine Learning Methods for Natural Hazard Assessments)
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22 pages, 9529 KiB  
Article
Spatial Accessibility Analysis of Emergency Shelters with a Consideration of Sea Level Rise in Northwest Florida
by Jieya Yang, Onur Alisan, Mengdi Ma, Eren Erman Ozguven, Wenrui Huang and Linoj Vijayan
Sustainability 2023, 15(13), 10263; https://0-doi-org.brum.beds.ac.uk/10.3390/su151310263 - 28 Jun 2023
Cited by 2 | Viewed by 1182
Abstract
Hurricane-induced storm surge and flooding often lead to the closures of evacuation routes, which can be disruptive for the victims trying to leave the impacted region. This problem becomes even more challenging when we consider the impact of sea level rise that happens [...] Read more.
Hurricane-induced storm surge and flooding often lead to the closures of evacuation routes, which can be disruptive for the victims trying to leave the impacted region. This problem becomes even more challenging when we consider the impact of sea level rise that happens due to global warming and other climate-related factors. As such, hurricane-induced storm surge elevations would increase nonlinearly when sea level rise lifts, flooding access to highways and bridge entrances, thereby reducing accessibility for affected census block groups to evacuate to hurricane shelters during hurricane landfall. This happened with the Category 5 Hurricane Michael which swept the east coast of Northwest Florida with long-lasting damage and impact on local communities and infrastructure. In this paper, we propose an integrated methodology that utilizes both sea level rise (SLR) scenario-informed storm surge simulations and floating catchment area models built in Geographical Information Systems (GIS). First, we set up sea level rise scenarios of 0, 0.5, 1, and 1.5 m with a focus on Hurricane Michael’s impact that led to the development of storm surge models. Second, these storm surge simulation outputs are fed into ArcGIS and floating catchment area-based scenarios are created to study the accessibility of shelters. Findings indicate that rural areas lost accessibility faster than urban areas due to a variety of factors including shelter distributions, and roadway closures as spatial accessibility to shelters for offshore populations was rapidly diminishing. We also observed that as inundation level increases, urban census block groups that are closer to the shelters get extremely high accessibility scores through FCA calculations compared to the other block groups. Results of this study could guide and help revise existing strategies for designing emergency response plans and update resilience action policies. Full article
(This article belongs to the Special Issue Utilizing Advanced Spatial Analysis and Machine Learning Methods for Natural Hazard Assessments)
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