Early Natural Disaster Prediction Using Machine Learning: A Comprehensive Review

Early Natural Disaster Prediction Using Machine Learning: A Comprehensive Review

Himanshu Yadav 1, Vivek Padavale 2, Harshal Parate 3

1 Ai&Ds, AISSMS IOIT, Pune, Maharastra, India

2 Ai&Ds, AISSMS IOIT, Pune, Maharastra, India

3 Ai&Ds, AISSMS IOIT, Pune, Maharastra, India

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Abstract - In many parts of the world, climate change has caused floods, earthquakes, cyclones, wildfires, and landslides that are more frequent and violent than ever before, and human activity is making them worse through deforestation and urbanization, threatening lives, economies, and ecosystems at previously unseen levels. The fallout from such events costs billions each year and forces millions from their homes — highlighting the need for better predictive tools to improve early warning systems that can trigger timely interventions to avoid human suffering and economic destruction. Into this space, machine learning (ML), a genuinely transformational technology, is operating on ever-larger, more heterogeneous data stores (space-borne satellite images (e.g. Landsat), Internet of Things (IoT) sensors networks, meteorological weather records, seismic observatory systems, hydrological valences, etc.) to both increase the precision of forecasting and lower the time from forewarned to foreclosure. This broad review collects a range of ML techniques from traditional supervised approaches – including support vector machines (SVMs), artificial neural networks (ANNs) and decision trees – to unsupervised methods such as K-means clustering and DBSCAN for anomaly detection, through to more advanced deep learning methods such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) with LSTM units and transformer architectures (e.g., ViT-B-32), as well as ensemble methods, like Random Forests and XGBoost, that aggregate many predictors together for higher robustness. Based on a review of an extensive literature (e.g. Alamri (2018), Mosavi et al. (2018) on surface water flooding prediction, Belenguer et al. ), and systematic ML methodologies (Thajudeen et al. Singh et al. (2024) on weather and climate forecasting; (2024) AI-IoT integration for geo-disaster management: Case studies related to earthquakes, Chamola et al. (2021) on disaster management applications, Tabassum et al. (2024) for wildfire detection, Mustafa et al. (2024) focusing on explainable deep learning and HeyCoach (2025) on real-world case studies—this study assesses the capacity of these models across various disaster types, their dependency on both essential data sources, and their performance in overcoming existing and future challenges. Significant challenges include data quality issues (e.g., completeness, noise, imbalances, e.g., the overrepresentation of common floods vs. rare landslides), computational complexity that hampers real-time deployment in resource-scarce areas, and model interpretability, with nontransparent “black-box” systems undermining trust with decision-makers and practitioners. Transformative strategies for overcoming these impediments include hybrid formulations that integrate statistical and machine learning (ML) models, transfer learning (to apply pre-trained models to data scarce scenarios), IoT-AI integrations for real-time assessments, and explainable AI (XAI) mechanisms (e.g., Grad-CAM, LIME) that clarify model decision-making processes. Real-world applications, including Google’s flood prediction in South Asia and wildfire detection in California, showcase the practical impact and scalability of ML. Going forward, further studies must emphasise real-time melding of data sources for fluid entry of dynamic inputs; scalable methods like edge computing to enable reach in low-resource settings; and improved interpretability to build confidence among stakeholders, enhancing global early warning systems and ensuring reduced human and financial costs of natural disasters.