Machine learning can be applied to natural disaster prediction and prevention by analyzing large datasets such as satellite imagery, weather patterns, and geological data to identify early warning signs. Algorithms can be trained to detect anomalies and forecast events like earthquakes, floods, and wildfires with greater accuracy. Additionally, machine learning can enhance decision-making by modeling disaster scenarios, optimizing resource allocation, and guiding evacuation planning to mitigate risks.

It tends to be preferred to avoid the phrase "natural disaster":

1. Book https://global.oup.com/academic/product/disaster-by-choice-9780198841357

2. Paper https://doi.org/10.1016/j.pdisas.2019.100008

3. Commentary https://www.psychologytoday.com/blog/disaster-choice/202005/does-nature-play-disaster-games

4. Blog https://www.psychologytoday.com/gb/blog/disaster-choice

5. Vlog https://www.youtube.com/watch?v=BlVyY1UAOnU&list=PLCuu5t_nsFKC5IbjnahIEoKHK8o2vegaZ

6. TEDx https://youtube.com/watch?v=AayasycLqqU

Thank you!

Applying machine learning (ML)in natural disaster prediction and prevention involves using data-driven models to detect patterns, forecast events, and support early warning systems, such as:

1. Disaster Prediction

Machine learning helps predict when, where, and how intensely a disaster might occur by analyzing large datasets.

a. Earthquakes

• Data used: Seismic activity records, geological fault line data.
• ML application: Time series analysis, anomaly detection to identify precursors of seismic activity.
• Model types: Support Vector Machines (SVM), Random Forests, Deep Neural Networks.

b. Floods

• Data used: Rainfall patterns, river levels, satellite images, soil moisture.
• ML application: Predict flood likelihood and severity in real time.
• Model types: Gradient Boosting, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks.

c. Cyclones & Hurricanes

• Data used: Historical storm paths, sea surface temperature, wind speed.
• ML application: Storm trajectory and intensity prediction.
• Model types: Convolutional Neural Networks (CNNs), ensemble models.

2. Disaster Prevention & Risk Reduction

Machine learning is used for preparedness planning, resource allocation, and risk mapping.

a. Early Warning Systems

• Use case: Automatically send alerts based on predictive models (e.g., floods, landslides).
• Example: ML integrated with Internet of Things (IoT) sensors.

b. Damage Assessment

• Use case: After a disaster, satellite imagery + ML can assess infrastructure damage.
• Tools: Google Earth Engine + image recognition models.

c. Risk Mapping

• Use case: Identify high-risk zones for future mitigation (e.g., where to build shelters).
• Data used: Historical disaster data, demographic vulnerability indicators.

d. Optimizing Disaster Response

• ML helps: Predict supply needs, optimize evacuation routes, and deploy first responders efficiently.

Popular ML Techniques Used

• Supervised Learning (e.g., regression, classification)
• Unsupervised Learning (e.g., clustering for risk zones)
• Deep Learning (e.g., image recognition for satellite data)
• Natural Language Processing (for real-time social media and news data during disasters)

Benefits

• Timely and accurate predictions
• Cost-effective planning and resource use
• Life-saving early warnings

Challenges

• Need for high-quality, real-time data
• Interpretability of complex models
• Infrastructure limitations in vulnerable regions

Google tapped billions of mobile phones to detect quakes worldwide — and send alerts

Study reveals how the tech behemoth is using the motions sensors on phones to expand quake warnings to more countries...

Article Global earthquake detection and warning using Android phones

https://www.nature.com/articles/d41586-025-02278-3