Study on Uncertainty Data Analysis for Common Natural Disaster Prediction in the U.S. Using Cloud Computing and Machine Learning

Abstract

Accurate natural disaster prediction requires the integration of heterogeneous environmental datasets and the ability to model uncertainty arising from sensor noise, incomplete observations, and inconsistent spatiotemporal coverage. This study proposes a cloud-based, uncertainty-aware machine learning framework designed to support large-scale prediction of hurricanes, floods, and wildfires in the United States. The framework incorporates distributed data ingestion, cloud-native preprocessing, multi-source feature engineering, and probabilistic learning to address the complexity and variability inherent in environmental data. Through a unified cloud workflow, heterogeneous datasets from NOAA, USGS, and NASA FIRMS are harmonized using temporal alignment, spatial normalization, and uncertainty-reduction strategies such as imputation, smoothing filters, and cross-source calibration. Model evaluation focuses on performance trends rather than fixed numerical benchmarks, examining how different learning algorithms respond to the characteristics of each disaster type. Results indicate that deep models and uncertainty-aware approaches are particularly effective for highly dynamic hazards such as hurricanes and wildfires, while tree-based models perform well for structured hydrological predictions. A real-world wildfire event is used to illustrate the practical applicability of the framework. Overall, the proposed approach enhances robustness, interpretability, and operational relevance, demonstrating the value of combining cloud computing with uncertainty-aware machine learning for multi-hazard disaster prediction.