Feature Papers of Natural Hazards in 2022

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 2827

Special Issue Editor

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Guest Editor
Italian Space Agency (ASI), Via del Politecnico snc, 00133 Rome, Italy
Interests: earth observation; radar and optical remote sensing; InSAR; time series analysis; Earth Sciences; environmental geology; natural hazards; urban environments; geoheritage; geoconservation; cultural heritage
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Special Issue Information

Dear Colleagues,

As Editor-in-Chief of Section Natural Hazards, I am pleased to announce this collection, entitled “Feature Papers of Section Natural Hazards in 2022”. This Special Issue will be a collection of high-quality reviews and original papers from editorial board members, guest editors, and leading researchers, discussing new knowledge or new cutting-edge developments of Natural Hazards including but not limited to the following topics:

  • Geological hazards: earthquakes; volcanoes; and tsunamis;
  • Climate-change-related hazards: droughts, extreme heat, soil erosion, coastal erosion, etc.;
  • Hydro-meteorological hazards: hurricanes, typhoons, cyclones, and tornadoes; thunderstorms, sand/dust storms, river floods, flash floods, etc.;
  • Sea and ocean hazards: geohazards in lacustrine settings; hazards in coastal areas, extreme events in sea waves, etc.;
  • Mass-movement hazards: landslides, rock falls, debris flows, etc.;
  • Anthropogenic hazards: urban fires, air pollution, induced earthquakes, etc.

Dr. Deodato Tapete
Guest Editor

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. Geosciences is an international peer-reviewed open access monthly 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 1800 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.

Published Papers (1 paper)

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20 pages, 5629 KiB  
Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes’ Importance Ranking in Wildfire Susceptibility
by Andrea Trucchia, Hamed Izadgoshasb, Sara Isnardi, Paolo Fiorucci and Marj Tonini
Geosciences 2022, 12(11), 424; https://doi.org/10.3390/geosciences12110424 - 18 Nov 2022
Cited by 3 | Viewed by 2087
Susceptibility mapping represents a modern tool to support forest protection plans and to address fuel management. With the present work, we continue with a research framework developed in a pioneristic study at the local scale for Liguria (Italy) and recently adapted to the [...] Read more.
Susceptibility mapping represents a modern tool to support forest protection plans and to address fuel management. With the present work, we continue with a research framework developed in a pioneristic study at the local scale for Liguria (Italy) and recently adapted to the national scale. In these previous works, a random-forest-based modeling workflow was developed to assess susceptibility to wildfires under the influence of a number of environmental predictors. The main novelties and contributions of the present study are: (i) we compared models based on random forest, multi-layer perceptron, and support vector machine, to estimate their prediction capabilities; (ii) we used a more accurate vegetation map as predictor, allowing us to evaluate the impacts of different types of local and neighboring vegetation on wildfires’ occurrence; (iii) we improved the selection of the testing dataset, in order to take into account the temporal variability of the burning seasons. Wildfire susceptibility maps were finally created based on the output probabilistic predicted values from the three machine-learning algorithms. As revealed with random forest, vegetation is so far the most important predictor variable; the marginal effect of each type of vegetation was then evaluated and discussed. Full article
(This article belongs to the Special Issue Feature Papers of Natural Hazards in 2022)
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