Special Issue "Innovative Approaches for Environmental and Natural Hazard Forecasting: Proposals from Theory to Practice"

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Environmental Forecasting".

Deadline for manuscript submissions: closed (31 August 2021).

Special Issue Editor

Special Issue Information

Dear Colleagues,

The prediction of future natural or anthropic catastrophes is one of the greatest scientific challenges of our society in the 21st century. Territorial management of protected spaces or densely populated urban areas requires anticipating possible dangers. Mitigation of the risk of fires, floods, or earthquakes, among others, is a discipline in which advances in new prediction tools are made every day. This Special Issue seeks contributions involving innovative approaches or relevant case studies regarding environmental anthropic dangers and natural hazard forecasting in topics such as:

- Desertification and drought of semi-arid regions

- Loss of natural values of protected spaces

- Increased risks associated with climate change

- Wildfire danger in forests and periurban areas

- Assessment of future flood risks associated with anthropogenic actions

- Analysis of the seismic vulnerability of urban areas

Innovative methodologies, frameworks, or significant results from relevant case studies related to all these topics are welcome, but similar ones may also be considered for publication if they fit within the scope of this Special Issue.

Dr. Salvador García-Ayllón Veintimilla
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 papers will be 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. Forecasting is an international peer-reviewed open access quarterly 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 1000 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

  • environmental forecasting
  • natural hazard risks
  • earthquake vulnerability
  • flooding
  • wildfire danger
  • climate change prediction

Published Papers (2 papers)

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Research

Article
Gutenberg–Richter B-Value Time Series Forecasting: A Weighted Likelihood Approach
Forecasting 2021, 3(3), 561-569; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3030035 - 06 Aug 2021
Viewed by 368
Abstract
We introduce a novel approach to estimate the temporal variation of the b-value parameter of the Gutenberg–Richter law, based on the weighted likelihood approach. This methodology allows estimating the b-value based on the full history of the available data, within a data-driven setting. [...] Read more.
We introduce a novel approach to estimate the temporal variation of the b-value parameter of the Gutenberg–Richter law, based on the weighted likelihood approach. This methodology allows estimating the b-value based on the full history of the available data, within a data-driven setting. We test this methodology against the classical “rolling window” approach using a high-definition Italian seismic catalogue as well as a global catalogue of high magnitudes. The weighted likelihood approach outperforms competing methods, and measures the optimal amount of past information relevant to the estimation. Full article
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Article
Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network
Forecasting 2021, 3(1), 17-36; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010002 - 04 Jan 2021
Cited by 1 | Viewed by 1013
Abstract
Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even [...] Read more.
Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field, and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progressively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a recurrent neural network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes. Full article
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