Extreme Weather Prediction and Alert System with Machine Learning

DOI

10.2991/978-94-6463-738-0_88How to use a DOI?

Keywords

Machine Learning; LSTM; Random Forest; Time Series Forecasting; Real-Time Data; Disaster Management; Weather Alerts; Public Safety; Data Visualization

Abstract

Extreme weather conditions such as storms, heavy rain, and heat bring danger to people, equipment, and the environment. Therefore, this project looks at an Extreme Weather Prediction and Alert System that uses machine learning algorithms to predict these events and send alerts in real-time. In this case, a Random Forest algorithm is used to select pertinent features for predicting time series data using the Long Short Term Memory networks that perform better with historical weather information in coming up with future estimates. This also means that the essential elements of weather are taken into account first, which improves the accuracy of the predictions. The data is trained on the historical weather data available on a larger scale and updated in real-time weather data using an API to make it possible to have forecasts a few hours before the actual weather phenomena begin. The system is designed to issue a warning to members of the general public so that they can prepare for any impending disasters. In addition, accurate forecasts and weather model evaluation results will include extensive imagery as part of the project. This system is designed to improve forecasting of extreme weather events, aiming to improve disaster management and mitigating measures.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P. Mukeshkumar
AU  - A. Balachandar
AU  - K. Mohanraj
AU  - R. Vanitha
PY  - 2025
DA  - 2025/06/22
TI  - Extreme Weather Prediction and Alert System with Machine Learning
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 1141
EP  - 1152
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_88
DO  - 10.2991/978-94-6463-738-0_88
ID  - Mukeshkumar2025
ER  -