AIMC Topic: Natural Disasters

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Multimodal Social Sensing for the Spatio-Temporal Evolution and Assessment of Nature Disasters.

Sensors (Basel, Switzerland)
Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as i...

Can Building "Artificially Intelligent Cities" Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar's Perspective.

Sensors (Basel, Switzerland)
In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the u...

Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs.

Risk analysis : an official publication of the Society for Risk Analysis
Artificial intelligence (AI) methods have seen increasingly widespread use in everything from consumer products and driverless cars to fraud detection and weather forecasting. The use of AI has transformed many of these application domains. There are...

Nature Disaster Risk Evaluation with a Group Decision Making Method Based on Incomplete Hesitant Fuzzy Linguistic Preference Relations.

International journal of environmental research and public health
Because the natural disaster system is a very comprehensive and large system, the disaster reduction scheme must rely on risk analysis. Experts' knowledge and experiences play a critical role in disaster risk assessment. The hesitant fuzzy linguistic...

[AI based Evaluation of Psychotrauma related to Lahars in the Commune of PrĂȘcheur in the French Antilles].

Sante mentale au Quebec
Objectives Natural disasters have a significant impact on mental health. Data collected from the population offer a unique opportunity for post-disaster monitoring to help identify psychological support needs. The aim of this study is: 1) to identify...

Using deep learning for acoustic event classification: The case of natural disasters.

The Journal of the Acoustical Society of America
This study proposes a sound classification model for natural disasters. Deep learning techniques, a convolutional neural network (CNN) and long short-term memory (LSTM), were used to train two individual classifiers. The study was conducted using a d...