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.
Both natural and man-made disasters are very impactful to the surroundings, infrastructure, and human life. Proper and timely forecasting of disasters is essential in the reduction of the disaster. The conventional disaster prediction models are based on analysis of previous history and simple statistical methods which could be not capable of offering real-time and adaptive decision-making options. The present paper includes an in-depth research concerning AI-based decision systems of real-time disaster predictions that combine the latest machine learning (ML), deep learning (DL), and real-time sensor networks. We suggest 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. The system resorts to ensemble learning, recurrent neural networks (RNNs), and spatiotemporal modeling and manages to predict the occurrence of a flood, earthquake, wildfire, and storm with high accuracy. The system is shown in one of the case studies that use actual real-time sensor data on the environment and satellite images to prove the efficiency of the system. It is shown that there is substantial increase in accuracy of prediction and response time over traditional systems. The given strategy is focused on scalability, flexibility, and resilience to different disaster risks. In addition, the combination of AI and Internet of Things (IoT) and Geographic Information System (GIS) allows developing a real-time decision support system that can support the government agencies, emergency responders, and communities with making proactive and data-driven decisions. The study indicates the possibilities of AI-powered systems in shifting disaster management to a predictive instead of a reactive and prevention system.
Pooja Agarwal· International Journal of Mod...· 0 citations
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.
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
Both natural and man-made calamities have major impacts to life, infrastructures, and economic stability across the globe. The rising rate and severity of catastrophes like earthquakes, floods, cyclones, landslides, wildfires and pandemics require the creation of sophisticated forecasting methods. Conventional ways of managing disasters tend to use isolated data sources and manual interpretation thereby providing restrictions on predictive accuracy and response frequency. The paper is 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. The proposed model optimizes the situational awareness and early warning capabilities by using machine and deep learning. The research presents a multi-layered data acquisition, data preprocessing, and data trending, feature extraction, and predictive modeling. Sir-complicated algorithms that include the use of random forest, support vector machines, Convolutional Neural Networks, and Long Short-term Memory networks are used to process spatio-temporal patterns. The real-world datasets were used to test the system and show better prediction accuracy and lower response time. Findings from experimental outcomes show that holistic data-driven systems work far much better than traditional single source methodologies. The results point out the significance of data integration and smart analytics in disaster risk management. The study will help develop resilient smart disaster management systems and form a basis on the future advancements in real-time forecasting and emergency response planning.
Emma Roberts· International Journal of Eme...· 0 citations
Coastal flooding is one of the most severe natural hazards, causing significant damage to human life, infrastructure,
and ecosystems in coastal regions. Accurate and timely flood prediction is essential for effective disaster preparedness and
mitigation. This research presents a Coastal Flood Prediction System based on Machine Learning techniques to improve the
accuracy and efficiency of flood forecasting. The proposed system utilizes environmental parameters such as rainfall, humidity,
sea level, and wind speed to predict the likelihood of flood occurrence. Data preprocessing techniques are applied to clean and
prepare the dataset, followed by the implementation of machine learning algorithms, including Logistic Regression, Decision
Tree, and Random Forest. Among these, the Random Forest algorithm demonstrates superior performance in terms of
prediction accuracy and reliability. The developed system is integrated into a user-friendly web application using Python and
Flask, enabling users to obtain real-time flood predictions. Experimental results indicate that the proposed model effectively
identifies flood-prone conditions and supports early warning decision-making. The study highlights the potential of machine
learning in disaster management and provides a scalable framework for future flood prediction systems. The proposed approach
can contribute to reducing the impact of floods by enabling proactive planning and timely response measures
K. T. Kumar, D. Bhargavi· International Journal for Re...· 0 citations
Forest fire prevention increasingly depends on translating ecological monitoring into earlier, more reliable decisions about ignition risk, fuel condition, spread potential, and management intervention. This critical review evaluates artificial intelligence (AI) for forest fire prevention through full-text extraction of core studies and contextual synthesis of foundational fire-science literature. The evidence base contains 179 unique references, including an AI-focused corpus, classical deterministic and probabilistic fire-danger and spread models, global ignition and lightning studies, remote-sensing and fuel-moisture foundations, decision-support tools, and governance literature. We define prevention-facing AI as systems that support pre-ignition or pre-escalation decisions and compare studies by data source, model design, validation protocol, forecast horizon, transferability, interpretability, and management action. 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. AI is therefore most useful when it is hybrid, interpretable, and deployment-aware: it should complement established fire-weather and spread-model baselines while converting ecological observations into timely and actionable prevention judgments.