2024· International Journal of Modern Innovations and Emerging Trends· 0 citations
TL;DR
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.
Abstract
Natural and human-induced disasters are increasing in frequency and severity due to climate change, rapid urbanization, environmental degradation, and population growth. Conventional disaster prediction methods often lack the speed and accuracy needed for real-time emergency response. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Big Data Analytics enable intelligent systems to analyze diverse real-time data from satellites, IoT sensors, weather stations, seismic networks, GIS, and social media for accurate disaster forecasting. This paper presents an AI-based decision support framework integrating data acquisition, preprocessing, feature engineering, machine learning, deep learning, and automated decision-making within a scalable cloud-edge architecture. The study also reviews existing AI-based disaster prediction approaches, identifies their limitations, and compares their performance. 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.
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
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
The increasing frequency and severity of natural disasters have created a growing demand for intelligent systems capable of rapidly assessing disaster impacts and supporting emergency response operations. This study presents a comprehensive analysis of recent artificial intelligence (AI) techniques applied to disaster damage assessment using satellite imagery, unmanned aerial vehicle (UAV) data, remote sensing products, and geospatial information. The reviewed studies encompass machine learning, deep learning, hybrid and transfer learning, and ensemble learning approaches across diverse disaster scenarios, including floods, landslides, earthquakes, wildfires, cyclones, and sinkholes. The analysis reveals that deep learning and ensemble learning techniques generally achieve superior predictive performance for disaster classification, detection, and damage mapping, while machine learning approaches offer advantages in interpretability and computational efficiency. The study further identifies key challenges, including dataset imbalance, limited geographical diversity, poor cross-regional generalization, high computational requirements, insufficient multimodal data integration, and difficulties in real-time deployment. Based on these findings, a conceptual framework for next-generation AI-driven disaster assessment is proposed, emphasizing multimodal data fusion, explainable AI, adaptive learning, and real-time decision support. The study provides comparative insights into existing methodologies, highlights critical research gaps, and outlines future directions for developing scalable, interpretable, and operationally effective disaster management systems.
Rekha M. Pillai, S.Suprakash· 2026 7th International Confe...· 0 citations
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.
EL-Alfy A.E, Esmat Mona, Sakr Hagar· International Journal of Sci...· 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
The increasing frequency and intensity of natural and man-made disasters have highlighted the necessity for intelligent disaster management systems capable of providing rapid response and accurate situational awareness. Conventional disaster management approaches often rely on manual observations, fragmented communication infrastructures, and delayed reporting mechanisms, which can significantly reduce the effectiveness of emergency response operations. The emergence of the Internet of Things (IoT) has introduced new opportunities for real-time monitoring, data acquisition, predictive analytics, and automated decision-making. This paper presents an IoT-enabled disaster management system that integrates distributed sensors, wireless communication networks, cloud computing platforms, and machine learning techniques to improve disaster preparedness, detection, response, and recovery. The proposed framework continuously monitors environmental and structural parameters, analyzes collected information through intelligent algorithms, and generates early warnings for emergency authorities and affected communities. The system aims to minimize casualties, reduce property damage, and enhance coordination among disaster response agencies. Experimental evaluation demonstrates improved prediction accuracy, reduced response time, and enhanced operational efficiency when compared with conventional disaster management systems. The proposed solution offers a scalable, reliable, and cost-effective approach for building resilient smart cities and disaster-resistant communities.
Keywords— Internet of Things, Disaster Management, Smart Cities, Early Warning Systems, Machine Learning, Cloud Computing, Emergency Response.
Kasiraju Rajvardhan Kasiraju Rajvardhan, Islavath Meenakshi Islavath Meenakshi, A. M. A Mamatha· International Journal of Cre...· 0 citations
