Topic Editors

Perception, Robotics, and Intelligent Machines Research Group (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada
Department of Geomatics Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada

AI for Natural Disasters Detection, Prediction and Modeling

Abstract submission deadline
25 April 2025
Manuscript submission deadline
25 July 2025
Viewed by
761

Topic Information

Dear Colleagues,

In recent years, we have witnessed escalating climate change and its increasing impact on global ecosystems, human lives, and the world economy. This situation calls for advanced tools that can leverage artificial intelligence (AI) for the early detection, prediction, and modeling of natural disasters. The increasing frequency and intensity of events such as wildfires, flooding, storms, and other catastrophic incidents necessitate innovative approaches for mitigation and response. This call for papers invites contributions that address the critical aspects of this interesting field, focusing on the integration of AI methodologies with remote sensing data. We encourage submissions that span a wide range of topics, including reviews of state-of-the-art AI applications for natural disaster management, risk assessment and hazard prediction; the use of AI to detect and track specific events; modeling techniques employing AI; and the development of advanced forecasting models utilizing AI methodologies.

The aim of this call is to bring together researchers and experts from various areas to foster collaborative efforts in developing cutting-edge solutions that will enhance our ability to anticipate, understand, and respond to the increasing challenges posed by natural disasters in an era of climate change.

Dr. Moulay A. Akhloufi
Dr. Mozhdeh Shahbazi
Topic Editors

Keywords

  • AI for natural disasters
  • forest fires, flooding, storms, earthquakes
  • forest monitoring, environmental monitoring, natural risks
  • forecasting models, mitigation, and response
  • earth observation, remote sensing
  • multispectral, hyperspectral, LiDAR, photogrammetry
  • machine learning, deep learning, data fusion, image processing
  • mapping, modelling, digital twins

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai
- - 2020 20.8 Days CHF 1600 Submit
Big Data and Cognitive Computing
BDCC
3.7 4.9 2017 18.2 Days CHF 1800 Submit
Fire
fire
3.2 3.5 2018 15 Days CHF 2400 Submit
GeoHazards
geohazards
- - 2020 20.7 Days CHF 1000 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit

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Published Papers (1 paper)

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12 pages, 3871 KiB  
Article
Multitemporal Dynamics of Fuels in Forest Systems Present in the Colombian Orinoco River Basin Forests
by Walter Garcia-Suabita, Mario José Pacheco and Dolors Armenteras
Fire 2024, 7(6), 171; https://doi.org/10.3390/fire7060171 - 21 May 2024
Viewed by 351
Abstract
In Colombia’s Orinoco, wildfires have a profound impact on ecosystem dynamics, particularly affecting savannas and forest–savanna transitions. Human activities have disrupted the natural fire regime, leading to increased wildfire frequency due to changes in land use, deforestation, and climate change. Despite extensive research [...] Read more.
In Colombia’s Orinoco, wildfires have a profound impact on ecosystem dynamics, particularly affecting savannas and forest–savanna transitions. Human activities have disrupted the natural fire regime, leading to increased wildfire frequency due to changes in land use, deforestation, and climate change. Despite extensive research on fire monitoring and prediction, the quantification of fuel accumulation, a critical factor in fire incidence, remains inadequately explored. This study addresses this gap by quantifying dead organic material (detritus) accumulation and identifying influencing factors. Using Brown transects across forests with varying fire intensities, we assessed fuel loads and characterized variables related to detritus accumulation over time. Employing factor analysis, principal components analysis, and a generalized linear mixed model, we determined the effects of various factors. Our findings reveal significant variations in biomass accumulation patterns influenced by factors such as thickness, wet and dry mass, density, gravity, porosity, and moisture content. Additionally, a decrease in fuel load over time was attributed to increased precipitation from three La Niña events. These insights enable more accurate fire predictions and inform targeted forest management strategies for fire prevention and mitigation, thereby enhancing our understanding of fire ecology in the Orinoco basin and guiding effective conservation practices. Full article
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