Artificial neural network approaches for disaster management: A literature review
Disaster management (DM) is one of the leading fields that deal with the humanitarian
aspects of emergencies. The field has attracted researchers because of its ever-increasing …
aspects of emergencies. The field has attracted researchers because of its ever-increasing …
A new approach based on tensorflow deep neural networks with adam optimizer and gis for spatial prediction of forest fire danger in tropical areas
TX Truong, VH Nhu, DTN Phuong, LT Nghi, NN Hung… - Remote Sensing, 2023 - mdpi.com
Frequent forest fires are causing severe harm to the natural environment, such as
decreasing air quality and threatening different species; therefore, developing accurate …
decreasing air quality and threatening different species; therefore, developing accurate …
Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms
Flood is considered as the most devastating natural hazards that cause the death of many
lives worldwide. The present study aimed to predict flood vulnerability for Warsaw, Poland …
lives worldwide. The present study aimed to predict flood vulnerability for Warsaw, Poland …
A review on natural disaster detection in social media and satellite imagery using machine learning and deep learning
A disaster is a devastating incident that causes a serious disruption of the functions of a
community. It leads to loss of human life and environmental and financial losses. Natural …
community. It leads to loss of human life and environmental and financial losses. Natural …
How do climate risks impact the contagion in China's energy market?
Energy security is a critical facet of national security, especially in energy-importing
countries, and significant fluctuations in the energy market can profoundly influence the real …
countries, and significant fluctuations in the energy market can profoundly influence the real …
[HTML][HTML] Characterizing temporal trends of meteorological extremes in Southern and Central Ontario, Canada
L Shah, CA Arnillas, GB Arhonditsis - Weather and Climate Extremes, 2022 - Elsevier
Forecasts of increased frequency of meteorological extremes have received considerable
attention due to their potential impact on the integrity of biotic communities, stability of …
attention due to their potential impact on the integrity of biotic communities, stability of …
Data-driven community flood resilience prediction
Climate change and the development of urban centers within flood-prone areas have
significantly increased flood-related disasters worldwide. However, most flood risk …
significantly increased flood-related disasters worldwide. However, most flood risk …
Interpretable data-driven model for Climate-Induced Disaster damage prediction: The first step in community resilience planning
The frequency and magnitude of Climate-Induced Disasters (CID) have been increasing
consistently over the past few decades. Alleviating the impacts of such disasters is thus …
consistently over the past few decades. Alleviating the impacts of such disasters is thus …
[HTML][HTML] Facing climate change and improving emergency responses in Southern America by analysing urban cyclonic wind events
R Pérez-Arévalo, JL Serrano-Montes… - Urban Climate, 2023 - Elsevier
Climate change is modifying the spatiotemporal patterns of global precipitation events,
temperatures, and winds, therefore, after extreme events, improving emergency responses …
temperatures, and winds, therefore, after extreme events, improving emergency responses …
[Retracted] Prediction and Risk Assessment of Extreme Weather Events Based on Gumbel Copula Function
PH Yang, Y Yu, F Gu, MJ Qu… - Journal of Function …, 2022 - Wiley Online Library
Damage caused by climate catastrophes is severe, especially for the 1‐in‐100‐year events.
This study is aimed at assessing the frequency and spatiotemporal regularity of extreme …
This study is aimed at assessing the frequency and spatiotemporal regularity of extreme …