Abstract
The transformational field of utilizing machine learning (ML) and artificial intelligence (AI) to forecast natural disasters is explored in this book chapter. The severity of natural disasters demands catastrophe mitigation, risk assessment, and early warning. The use of AI and ML technologies, which have the potential to safeguard communities, is essential in this endeavor. The chapter emphasizes the need of a multidisciplinary strategy that combines domain expertise with AI and ML to improve capacity to predict and respond to natural disasters with an ultimate goal to build a more secure and resilient global community. The chapter examines a number of AI and ML applications in disaster prediction, including forecasts for earthquakes, floods, wildfires, hurricanes, landslides, tsunamis etc. In order to increase prediction accuracy, it covers sensor networks, data sources, and the integration of various datasets. Additionally, it tackles the issues related to ethical considerations, robustness of the model, and data quality.
TopIntroduction
In the past few years, there have been more and worse natural disasters and extreme weather events (Leng et al., 2023). This is a big problem for people and governments all over the world because it can hurt or even kill people, damage buildings and things, and make it hard for businesses to work. To make things better, we need to be able to predict and get ready for these disasters. Using special computer programs called machine learning algorithms, scientists have been able to get better at predicting the weather and natural disasters. These algorithms can look at a lot of information, like pictures from space and past weather records, to figure out what might happen in the future (Jain et al., 2023). They can even predict things like heatwaves or droughts by looking at things like temperature and rain. Machine learning algorithms also help scientists predict natural disasters, like earthquakes or floods. They can use different kinds of algorithms to make these predictions. More and more scientists are using these algorithms to study and predict the weather and natural disasters because they can help keep people safe (Aybar-Ruiz et al., 2016).
These special computer programs can look at different kinds of information, like earthquake data, past disaster information, and weather data, to guess what might happen in a natural disaster. For example, they can guess if there might be an earthquake or a hurricane by looking at things like how the ground is moving, how fast the wind is blowing, and how warm the ocean is. Using these programs can help governments and organizations be ready for disasters and keep people safe. They can give us warnings about upcoming disasters so we can go to safer places (Zhang et al., 2022a). They can also help decide where to send resources and plan the best ways for people to leave dangerous areas. But these programs aren't always perfectly accurate. Sometimes they give false alarms or their predictions are affected by the quality of the information they use (Jia et al., 2022). We're studying these programs to see how we can make them better and help prepare for disasters around the world.
Figure 1. ML in disaster management
Predictive analytics in machine learning for disaster management could be used to notify people about impending disasters. In addition to forecasts, it can help rescue crews by providing information on the extent of damage as shown in the Figure 1.This information has details about how to do something, where the information comes from, what the results are, examples of how it works, and important lessons we learned. Some experts did a really good job of studying this and made sure it was accurate. It helps us understand how machines can help protect people and places from natural disasters all over the world (Esrafilian-Najafabadi & Haghighat, 2022).