Artificial Intelligence in Disaster Prediction and Response

Sonali Mondal

Assistant Professor

CS & IT Department

Kalinga University

ABSTRACT

The main aim of disaster prediction is to predict in advance from earthquake, floods, hurricane etc. Early prediction will help us to prepare ourselves for the situation. We can use ML language for the accurate forecasting of various natural disasters. Using of historical data, environmental variables, studying of these techniques will helps us for forecasting and classifying these events. Through a comprehensive survey that the use of ML methods, random forest methods, support vector machines (SVM), neural networks and their limitations will help us for predicting specific types of natural disaster. Artificial intelligence is widely employed in disaster preparedness and forecasting, damage, mitigation, and reduction will help better and faster response to disaster [1]. This chapter will look at that how Artificial intelligence technologies can be used to lessen the effect of various disaster and will also tell that how Artificial intelligence is helping by providing information and the technologies that will help from various disaster.

INTORDUCTION

This article defines some important role of Artificial Intelligence (AI) for predicting the natural disaster, highlighting how the advanced machine learning models and data analytics making changes disaster preparedness and response. The main help we will get by analyzing and exploring the traditional and old methods of disaster prediction and knowing their limitations for adapting them. Using all these we can approach, showcasing how large dataset, including, satellite imaginary, climate models, and real time sensor data are being processed all the data are being through AI to use from predicting various effects like earthquake, floods, and hurricanes.

We can use these AI techniques, such as deep learning, neutral works, and natural language processing for examined the various disaster prediction. Through this chapter we can go through all the real cases studies to illustrate the work of AI in disaster prediction and also to demonstrate that how the technologies have been successfully worked to mitigate the impact of all the natural disaster. Another way of preventing disaster management is coordination and collaboration among different agencies and stakeholders like private sector, government agencies, non- governmental organization, and the affected peoples from the disasters.The integration of different technologies such as early warning system and Geographic Information System (GIS) will help to predict, monitor and to respond disaster.

Disaster management is proactive and adaptive approach for us to prepare ourselves for the upcoming challenges in future or in present time. Machine learning has help us in various ways like in healthcare, transportation, finance etc. However, it is in the realm of disaster management where its use and need are much more [2].

This article will delves the importance of AI and Machine Learning for predicting or analyzing the natural disasters and also how to face the challenges.

DISASTER

Preparation Disaster Management Response Cycle 

After Event Before Event

Mitigation  Recovery

 LITREATURE REVIEW

Disaster management, it is a very critical field for dealing with the natural and man-made disasters, has relied on historical data, experience. AI and ML has offered variety of tools that can help on large – scale data and make the prediction in such a way that tradition systems cannot do but the modern technologies can do. The main work of these technologies is to focus in the natural disaster such as floods, hurricanes, and, wildfires.

Among all this disaster floods are the most common natural disaster causing damage to life and to our properties. The main cause of flood disaster is influenced by water flow, rainfall, river conditions and soil moisture. In this machine learning techniques, such as support vector machine (SVM), decision tree, and neural networks is being used widely in flood prediction. Studies has shown us that how AI models can process data from multiple sources- such as satellite imagery, weather forecasts [3].

Disaster response optimization using AI and ML have been crucial in improving the effectiveness of disaster response system, and evacuating problems.AI based models have been designed to optimize the resources for distribution and ensuring the most effected population.AI is also been used for evacuating planning, mainly the machine learning models can predict the most effective evacuation routes based on real data about the traffic, infrastructure damage and the population movement.  A sensor network in the disaster area has provided many environmental data such as temperature, humidity, and water levels [4].

The main ethical challenges are provided by AI and ML is that they offer potential for disaster management, and, they provide and introduce many different challenges and this include like data privacy concerns, algorithms, and the need of transparency [5]. The AI models those work mainly on historical data will lead to allocate and to response disaster. As research in this field AI and ML will play a very important role for predicting and to manage the impacts of disasters.

CONCLUSION

AI and Machine learning have so much potential that they can transform disaster management by providing more and more accurate predictions and real time insights, and proactive solutions. These technologies can help us to reduce the effect of disaster by early warning systems, and data driven decision – making. The challenges such as data quality and some ethical concerns need to be used and the benefits of AI and ML in disaster management are significant, and this will lead to improve faster response and will reduce the loss.

Reference

[1] Satishkumar, D., & Sivaraja, M. (Eds.). (2024). Utilizing AI and Machine Learning for Natural Disaster Management. IGI Global.

[2] Ghaffarian, S., Taghikhah, F. R., & Maier, H. R. (2023). Explainable artificial intelligence in disaster risk management: Achievements and prospective futures. International Journal of Disaster Risk Reduction, 98, 104123.

[3] Ogie, R. I., Rho, J. C., & Clarke, R. J. (2018, December). Artificial intelligence in disaster risk communication: A systematic literature review. In 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) (pp. 1-8). IEEE.

[4] Thekdi, S., Tatar, U., Santos, J., & Chatterjee, S. (2023). Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities. Risk analysis, 43(8), 1641-1656.

[5] Ghaffarian, S., Roy, D., Filatova, T., & Kerle, N. (2021). Agent-based modelling of post-disaster recovery with remote sensing data. International journal of disaster risk reduction, 60, 102285.