Topic Editors
Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience
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
|
- | 1.6 | 2020 | 17.6 Days | CHF 1200 | Submit |
GeoHazards
|
- | - | 2020 | 20.7 Days | CHF 1000 | Submit |
ISPRS International Journal of Geo-Information
|
3.4 | 6.2 | 2012 | 35.5 Days | CHF 1700 | Submit |
Land
|
3.9 | 3.7 | 2012 | 14.8 Days | CHF 2600 | Submit |
Remote Sensing
|
5.0 | 7.9 | 2009 | 23 Days | CHF 2700 | Submit |
Smart Cities
|
6.4 | 8.5 | 2018 | 20.2 Days | CHF 2000 | Submit |
Infrastructures
|
2.6 | 4.3 | 2016 | 16.9 Days | CHF 1800 | Submit |
Automation
|
- | - | 2020 | 26.3 Days | CHF 1000 | Submit |
MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:
- Immediately share your ideas ahead of publication and establish your research priority;
- Protect your idea from being stolen with this time-stamped preprint article;
- Enhance the exposure and impact of your research;
- Receive feedback from your peers in advance;
- Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.
