Jul 2026· International Journal of Scientific Research in Engineering & Technology· 0 citations
Abstract
Natural disasters with tremendous impacts on human lives, infrastructure, and ecosystems are frequent all over the world, which calls for intelligent, data-driven decision support systems for early diagnosis and effective crisis management. This paper demonstrates a Natural Disaster Diagnosis and Crisis Management System design and development featuring real-time environmental sensing, technological monitoring, and pre-emptive response planning within a unified decision-support framework. The proposed system include predict disasters using machine learning. It follows a modular architecture that integrates analytical risk assessment, formulation of preventive strategy, and dynamic action planning through an interactive GUI. NDDCMS structures disaster management into five operational phases, namely disaster diagnosis, early warning indicators, response during the disaster, post-disaster recovery, and preventive planning. Each phase integrates stepwise risk indicators and decision inputs to support officials and community response teams. It relies on quantifiable environmental parameters (e.g., precipitation, soil moisture, wind speed, and temperature fluctuation) and corresponding technological sensing mechanisms for the classification of risk levels and the improvement of early warning reliability. By focusing on a user-centered design and data-driven workflow, the system advances situational awareness, hastens decision-making, and closes the gap between disaster prediction and effective response. This framework enhances national and local-level disaster resilience by supporting viable risk reduction and crisis management strategies.
An approach based on multi-layered which involves combining real-time data collection, AI-enhanced predictive analytics, and automated decision-making to improve disaster preparedness and response is suggested to improve disaster preparedness and response.
Pooja Agarwal· International Journal of Mod...· 0 citations
The findings demonstrate that AI-driven disaster prediction systems significantly improve early warning capabilities, situational awareness, resource allocation, infrastructure protection, and emergency response, ultimately reducing disaster impacts and saving lives.
Alan Bundy, Karen Spärck Jones· International Journal of Mod...· 0 citations
Flood is one of the most destructive natural disasters in Tamil Nadu and needs proper forecasting systems to give early warning and mitigate the disaster. The present study, the Smart Flood Forecasting System is an AI and Machine Learning-powered system that incorporates four key datasets (Flood Inventory, Rainfall, Flood Impact, and IndoFlood events) with real-time weather data and automated voice notifications using Twilio. It used two complementary models: a Proposed Optimized Random Forest model, which was trained using curated datasets only, achieved 97.9% accuracy, 97.7% precision, 96.4% recall, and 97.1% F1-score using hyperparameter optimization and feature selection; and a Real Dataset framework, which used Logistic Regression, KNearest Neighbors (KNN), and The Flood Impact data added insights of the districts to the predictive features in terms of fatalities, injuries and the mean flood duration, enhancing the correlations between human displacement and the severity of floods. The high-risk cases identified during the risk assessment were more than 9,000 with a 60% probability threshold and automated voice alerts were successfully triggered in case of extreme flood scenarios. The system has integrated curated datasets, optimized algorithms, real-time weather integration, and instant communication mechanism, which makes it appear systematic, efficient, and scalable in disaster management to provide timely alerts and actionable insights to flood-prone areas in Tamil Nadu.
Anushya D, A. M· 2026 6th International Confe...· 0 citations
A full predictive framework of disasters based on the integrated data system as a conglomeration of satellite imagery, Internet of Things (IoT) sensor data, meteorological data, geospatial databases, social media feeds and historic disaster data is presented.
Emma Roberts· International Journal of Eme...· 0 citations
This research presents a Coastal Flood Prediction System based on Machine Learning techniques to improve the accuracy and efficiency of flood forecasting and provides a scalable framework for future flood prediction systems.
K. T. Kumar, D. Bhargavi· International Journal for Re...· 0 citations
The synthesis shows that AI is most mature for multimodal sensing, smoke/fire detection, susceptibility mapping, and short-horizon forecasting, but less mature for prospective decision-support validation, cross-ecosystem transfer, and operational accountability.