Topic Editors

Civil and Geo-Environmental Laboratory, Lille University, 59650 Villeneuve d'Ascq, France
Dr. Marwan Alheib
INERIS—French National Institute for Industrial Environment and Risks, Parc Technologique Alata—BP2, 60550 Verneuil-en-Halatte, France
Department of Project, Quality and Logistics Management, Faculty of Management, Wrocław University of Science and Technology, Smoluchowskiego 25, 50-370 Wrocław, Poland
Prof. Dr. Fadi Comair
Energy, Environment, Water and Research Centre, Cyprus Institute, Nicosia, Cyprus
Department of Civil, Energy, Environmental and Material Engineering, Mediterranean University of Reggio Calabria, 89124 Reggio Calabria, Italy
Prof. Dr. Xiongyao Xie
Department of Geotechnical Engineering, Tongji University, Shaghai, China
Prof. Dr. Yasin Fahjan
Civil Engineering, Istanbul Technical University, Maslak, Turkey
Dr. Salah Zidi
Hatem Bettaher Laboratory, IResCoMath, University of Gabes, Gabes 6029, Tunisia

Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience

Abstract submission deadline
31 March 2025
Manuscript submission deadline
30 June 2025
Viewed by
346

Topic Information

Dear Colleague,

Countries worldwide are subjected to new and complex challenges related to the intensification of the frequency and severity of natural disasters because of the impact of climate change, rapid demographic growth, and intense urbanization. These challenges have a significant socio-economic impact because of the large-scale damage due to natural disasters. Indeed, natural disasters generally cover large areas, causing substantial human losses, severe environmental damage, and destruction of infrastructures that support social and economic activity.

The latest advances in monitoring using IoT, crowdsourcing, satellites, and drones provide new opportunities to collect large amounts of data related to natural disasters.

The use of machine learning and big data enables the development of effective solutions that improve urban systems' resilience to natural disasters, including a better understanding of the response of complex socio-technical systems to natural disasters, the development of early warning systems, rapid scanning of damage, optimization of emergency actions, use of automation to reduce and protect critical infrastructures, and the adaptation of infrastructures to the new level of natural hazards.

The objective of this Topic is to share the latest developments in this area with a focus on the following questions:

  • What are the new scientific challenges related to the intensification of natural disasters (floods, earthquakes, storms, heat waves, disasters, wildfire and landslides)?
  • How could digital technology (IoT, crowdsourcing, and satellite) enhance natural disaster monitoring?
  • How could ML and BigData empower real-time analysis of data related to natural disasters?
  • How could ML and BigData improve the efficiency of early warning systems?
  • How could ML and BigData help adaptation strategies to natural disasters?
  • How could ML and BigData help reduce damage related to natural disasters?

Prof. Dr. Isam Shahrour
Dr. Marwan Alheib
Dr. Anna Brdulak
Prof. Dr. Fadi Comair
Dr. Carlo Giglio
Prof. Dr. Xiongyao Xie
Prof. Dr. Yasin Fahjan
Dr. Salah Zidi
Topic Editors

Keywords

  • big data
  • machine learning
  • artificial intelligence
  • crowdsourcing
  • IoT
  • Resilience
  • natural disaster
  • flood
  • earthquake
  • storms
  • landslide
  • wildfire
  • climate change
  • early warning
  • adaptation

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Earth
earth
- 1.6 2020 17.6 Days CHF 1200 Submit
GeoHazards
geohazards
- - 2020 20.7 Days CHF 1000 Submit
ISPRS International Journal of Geo-Information
ijgi
3.4 6.2 2012 35.5 Days CHF 1700 Submit
Land
land
3.9 3.7 2012 14.8 Days CHF 2600 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit
Smart Cities
smartcities
6.4 8.5 2018 20.2 Days CHF 2000 Submit
Infrastructures
infrastructures
2.6 4.3 2016 16.9 Days CHF 1800 Submit
Automation
automation
- - 2020 26.3 Days CHF 1000 Submit

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Published Papers

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