Abstract
In recent years, shifts in weather patterns have become increasingly apparent, leading to a rise in the frequency and severity of severe weather-related disruptive events across the globe. These events, which can include floods, storms, heavy rain, high winds, winter storms, heavy snow, and blizzards, pose a significant threat to public health and safety, as well as having negative economic impacts on key sectors such as agriculture, critical infrastructure, and emergency management. To address this challenge, our paper proposes a multi-modal learning approach for predicting and estimating risk for disruptions, by integrating weather- related data from multiple sources, including text and sensor recordings. Through experimental evaluation on a dataset of hourly weather data from three different climates - Alaska, Nevada, and Pennsylvania - we demonstrate that our approach outperforms alternatives that rely solely on weather recordings.
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Acknowledgements
This research was sponsored by the U. S. Army Engineer Research and Development Center (ERDC) and was accomplished under Cooperative Agreement Number W9132V-23-2-0002. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Engineer and Development Center (ERDC) or the U.S. Government. During this work, Mr. H. Otudi was funded by the College of Computer Science and Information Technology at Jazan University in Saudi Arabia.
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Otudi, H., Gupta, S., Obradovic, Z. (2024). Leveraging Diverse Data Sources for Enhanced Prediction of Severe Weather-Related Disruptions Across Different Time Horizons. In: Iliadis, L., Maglogiannis, I., Papaleonidas, A., Pimenidis, E., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2024. Communications in Computer and Information Science, vol 2141. Springer, Cham. https://doi.org/10.1007/978-3-031-62495-7_17
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