Abstract :

Natural Disasters, Including Floods, Earthquakes, Hurricanes, And Wildfires, Pose Severe Threats To Human Life, Critical Infrastructure, And Global Socioeconomic Stability. Traditional Empirical And Physical Forecasting Models Often Struggle To Process The Sheer Volume, Velocity, And Non-linear Complexity Of Modern Environmental Datasets. To Overcome These Limitations, This Paper Explores The Deployment Of Advanced Machine Learning (ML) And Deep Learning (DL) Algorithms To Revolutionize Disaster Prediction And Early Warning Systems. By Aggregating Heterogeneous Data Streams—such As Real-time Satellite Imagery, Seismic Sensor Readings, Historical Meteorological Records, And Geographic Information System (GIS) Data—machine Learning Models Can Discern Intricate, Pre-disaster Anomalies That Elude Human Analysts. Specifically, This Study Evaluates The Efficacy Of Supervised Learning Frameworks, Including Random Forests, Support Vector Machines (SVM), And Gradient Boosting, Alongside Deep Learning Architectures Like Long Short-Term Memory (LSTM) Networks For Sequential Time-series Forecasting. The Proposed Predictive Framework Integrates Live External Weather APIs And Automated Data Preprocessing Pipelines To Dynamically Assess Risk Metrics And Pinpoint Hazard-prone Zones. Furthermore, Natural Language Processing (NLP) Is Integrated Into The Architecture To Actively Mine Social Media Feeds And News Alerts, Translating Public Distress Signals Into Actionable Situational Awareness For Emergency Responders. Experimental Results Demonstrate That The Hybrid LSTM-optimization Model Achieves An Exceptional Prediction Accuracy Of Over 95%, Significantly Outperforming Conventional Statistical Forecasting Techniques. By Transforming Reactive Emergency Response Into Proactive Disaster Mitigation, This Research Provides Policymakers, Urban Planners, And Global Humanitarian Agencies With A Robust, Data-driven Framework. Ultimately, The Integration Of Artificial Intelligence In Disaster Management Mitigates Economic Losses, Optimizes Supply Chain Resource Allocation, And Establishes A Scalable Foundation For Building Climate-resilient Communities Worldwide.

Published:

22-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

1332-1340

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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