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Social Sensing of Natural Hazards and Extreme Weather
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Social Sensing of Natural Hazards and Extreme Weather

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (1 July 2021) | Viewed by 11725

Special Issue Editor

Dr. Rudy Arthur

E-Mail Website
Guest Editor
University of Exeter, Laver Building, North Park Road, Exeter, EX4 4QE, United Kingdom
Interests: interplay between geography; social networks

Special Issue Information

Dear Colleagues,

It has been 10 years since, in their path breaking work, Sakaki et. al. used Twitter to sense earthquakes and create an advance warning system that outperformed traditional meteorological systems. Since then, the ubiquity of social media has only increased, and previously ‘dark’ countries and regions have become active participants on Twitter, WhatsApp, Instagram, and other platforms. As well as the growing user base and widening geographic spread, the type of information shared has also changed, with images and video much more common now than 10 years ago. 

This Special Issue is focused on how the incredibly powerful and pervasive network of social sensors can be used to study natural hazards and extreme weather. Case studies on the social sensing of novel hazards or using previously unexplored social media platforms are encouraged. We are also interested in papers on algorithms that enable or improve social sensing methodology, such as location inference (from text or images) or novel software systems, e.g., for disaster response. We strongly encourage the submission of papers focusing on the keywords below, but works on related topics will also be considered.

Dr. Rudy Arthur
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. Sensors 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 2600 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

  • Social sensing
  • Social media
  • Natural hazards
  • Extreme weather
  • Natural catastrophes
  • Disaster response
  • Location inference
  • Now casting
  • Situational awareness
  • Environmental intelligence

Published Papers (2 papers)

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Research

16 pages, 1435 KiB  
Article
Social Sensing of Heatwaves
by James C. Young, Rudy Arthur, Michelle Spruce and Hywel T. P. Williams
Sensors 2021, 21(11), 3717; https://doi.org/10.3390/s21113717 - 26 May 2021
Cited by 10 | Viewed by 4176
Abstract
Heatwaves cause thousands of deaths every year, yet the social impacts of heat are poorly measured. Temperature alone is not sufficient to measure impacts and “heatwaves” are defined differently in different cities/countries. This study used data from the microblogging platform Twitter to detect [...] Read more.
Heatwaves cause thousands of deaths every year, yet the social impacts of heat are poorly measured. Temperature alone is not sufficient to measure impacts and “heatwaves” are defined differently in different cities/countries. This study used data from the microblogging platform Twitter to detect different scales of response and varying attitudes to heatwaves within the United Kingdom (UK), the United States of America (US) and Australia. At the country scale, the volume of heat-related Twitter activity increased exponentially as temperature increased. The initial social reaction differed between countries, with a larger response to heatwaves elicited from the UK than from Australia, despite the comparatively milder conditions in the UK. Language analysis reveals that the UK user population typically responds with concern for individual wellbeing and discomfort, whereas Australian and US users typically focus on the environmental consequences. At the city scale, differing responses are seen in London, Sydney and New York on governmentally defined heatwave days; sentiment changes predictably in London and New York over a 24-h period, while sentiment is more constant in Sydney. This study shows that social media data can provide robust observations of public response to heat, suggesting that social sensing of heatwaves might be useful for preparedness and mitigation. Full article
(This article belongs to the Special Issue Social Sensing of Natural Hazards and Extreme Weather)
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14 pages, 3535 KiB  
Article
Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network
by Muhammad Aamir, Tariq Ali, Muhammad Irfan, Ahmad Shaf, Muhammad Zeeshan Azam, Adam Glowacz, Frantisek Brumercik, Witold Glowacz, Samar Alqhtani and Saifur Rahman
Sensors 2021, 21(8), 2648; https://doi.org/10.3390/s21082648 - 09 Apr 2021
Cited by 30 | Viewed by 6839
Abstract
Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and [...] Read more.
Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Social Sensing of Natural Hazards and Extreme Weather)
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