Deep Learning for Extreme Weather Event Prediction and Early Warning Systems

EasyChair Preprint no. 14132

Abstract

The increasing frequency and severity of extreme weather events necessitate advanced prediction and early warning systems to mitigate their impact on human life and property. This research investigates the application of deep learning techniques to improve the accuracy and timeliness of extreme weather event predictions. By leveraging vast amounts of meteorological data, including satellite imagery, radar data, and historical weather records, we develop deep neural network models capable of identifying complex patterns and precursors to extreme weather phenomena such as hurricanes, tornadoes, and floods. The study focuses on various deep learning architectures, including convolutional neural networks (CNNs) for spatial data analysis and recurrent neural networks (RNNs) for temporal sequence modeling. We integrate these models into a real-time early warning system that provides actionable alerts to relevant authorities and the general public. The system's performance is evaluated based on prediction accuracy, lead time, and false alarm rates, with initial results showing significant improvements over traditional forecasting methods. This research highlights the potential of deep learning to enhance our understanding of extreme weather dynamics and offers a robust framework for developing more effective early warning systems, ultimately contributing to better disaster preparedness and response strategies.

Keyphrases: Convolutional Neural Networks, deep learning, disaster preparedness, Early Warning Systems, extreme weather prediction, Flood Prediction, hurricane prediction, Meteorological data, radar data, Real-time alerts, Recurrent Neural Networks, satellite imagery, tornado prediction

@Booklet{EasyChair:14132,
  author = {Joseph Oluwaseyi and Dylan Stilinki and Saleh Mohamed},
  title = {Deep Learning for Extreme Weather Event Prediction and Early Warning Systems},
  howpublished = {EasyChair Preprint no. 14132},

  year = {EasyChair, 2024}}