Aegis - Real-Time Disaster & Weather Alert System

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Aegis - Real-Time Disaster & Weather Alert System Aditya Singh1, Aryan Nayak2, Mrs. Mekhla Rai3, Dr. Pankaj Kumar4 1Student, Dept. of Computer Science & Engineering, Shri Ramswaroop College of Engineering & Management,

Lucknow, India

2Student, Dept. of Computer Science & Engineering, Shri Ramswaroop College of Engineering & Management,

Lucknow, India

3 Assistant Professor, Dept. of Computer Science & Engineering, Shri Ramswaroop College of Engineering &

Management, Lucknow, India

4 HOD, Dept. of Computer Science & Engineering, Shri Ramswaroop Collage of Engineering & Management,

Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - However, despite efforts being made towards

Also, among the critical services offered by Aegis is disaster recovery. Aegis is capable of evaluating the level of destruction in terms of magnitude and intensity. For instance, Aegis can evaluate satellite images to determine whether there is any destruction and change in the infrastructure.

In general, Aegis can be defined as a highly intelligent and effective tool that can potentially change our approach to managing disasters. Thanks to a set of innovative technologies and real-time data, the application can contribute to improving disaster management processes.

disaster management, natural disasters such as floods, earthquakes, and cyclones continue to threaten people’s lives and means of living in the world. There is no definite pattern that can be followed, and things may take a turn for worse instantly, and at that very moment, instant action should be taken against the situation. Although traditional methods used in disaster management have proven effective, they are not without any flaws as well. These include time delay in collecting and processing data, uncoordinated data sources, and ineffective communication channels. This problem can be solved through Aegis, a novel solution to disaster and weather alerts. This technology aims at enhancing all aspects of disaster management from prediction to recovery. The use of complex models such as LSTM and Random Forest in the Aegis makes possible the prediction of disasters based on the huge amount of data obtained in real time and stored over time. It turns out that the application of LSTM models is extremely effective for pattern recognition and trend analysis of geospatial and meteorological data. In addition, the software allows creating predictions of upcoming disasters based on satellite images, weather data, and other environmental indicators that could lead to floods, abnormal temperatures, earthquakes, etc.

Key Words: Artificial Intelligence, Machine Learning, Deep Learning, Early Warning Systems, Disaster Management, Real-time Detection, Geospatial Analysis.

1. INTRODUCTION Disasters like cloud bursts, floods, fires, and cyclones leave behind great human loss and economic destruction. Sudden change in climate and rapid urbanization contribute to the increase in frequency and intensity of these disasters, this highlights the need of disaster preparedness and response systems. Traditional methods of disaster management based on manual coordination and delayed coordination run the need for risk communication methods that afford for early warning, and the sender's information and decision-action processes. As globalization and technology facilitate the availability of risk communication methods for disaster management, systems based on Artificial Intelligence, Machine Learning, Deep Learning and Natural Language Processing offer a different transformative experience for real-time disaster management system.

It is the capability of live data processing and provision of realtime updates that differentiates this software from other similar technologies in the market. Map visualizations used in the application make it possible to estimate the severity of the situation through risk zones and affected locations. Moreover, location-based notifications can be received.

For enhanced communication in emergency situations, Aegis offers alerts through different means such as SMS, messaging apps, and notifications. With Aegis, the message will get to the users even when some forms of communication may not be available at the time. Besides, Aegis incorporates artificial intelligence to offer advice on safe evacuation routes and other measures that could help during a disaster. The suggestions from artificial intelligence are made based on the current situation and are very effective.

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Floods are happening a lot these days. They are getting worse. We need to find ways to detect floods early and do something to stop them. This plan is for a system that will help us manage floods. It will use real-time information to try to predict when floods will happen. The goal is to help people get ready for floods and reduce the damage to communities and the economy. This system will be different from what we have. It will use information about where and when floods likely to happen. This will help us prepare for floods in a way. We will use sources of information like

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sensors, satellite picture and models of water flow. This information will help us make maps of where floods likely to happen and when. We can use these maps to decide where to send help and who needs to be evacuated.

sequential learning strength. The study highlights the importance of early alerts and automated public warnings for timely disaster preparedness.

The system will also use computer programs to analyse how the weather, the shape of the land, and the buildings in an area affect the likelihood of a flood. This will help us make predictions about which areas most likely to flood. We can then use this information to decide when to send out warnings about floods. The system will be able to adjust the warnings based on how vulnerable different areas to floods.

This means we can avoid sending out false warnings about floods while still making sure everyone is safe from floods.

We want to make sure the system is working well. So, it will be able to change and adapt based on the information it gets about floods. This way we can make sure people get the warnings about floods at the time. Floods are a problem, and we need to do something about floods. This system is a step in the direction of dealing with floods. It will help us manage floods and keep people safe from floods.

An AI-powered disaster management system uses Machine Learning, Deep Learning, NLP, and Generative AI to enhance pre-disaster prediction, in-disaster monitoring, and postdisaster recovery [2]. It integrates USGS API for seismic activity tracking, automated mapping via Leaflet.js, and AIgenerated response plans sent via SMS to affected individuals. The study emphasizes automated damage assessment and personalized evacuation strategies as key improvements over traditional systems.

2.2 AI-Driven Disaster Management Platforms

2.3 Multi-Hazard Early Warning Systems (EWS) and Global Challenges The EWS study discusses several key elements including hazard monitoring, forecasting, mechanisms of communication, and preparedness activities. EWS systems are widely accepted globally as essential tools for managing disaster risk, however, less than half of all developing countries have a multi-hazard EWS. This has been ascribed to impediments that include limited infrastructure, sociocultural obstacles, insufficient financing, limited interoperability, and low-risk perception. Sustaining and developing EWS will require multi-disciplinary collaboration, data integration of risk-related datasets, and coverage for multi-hazards [3].

The Aegis – Real-Time Disaster & Weather Alert System capitalizes on these structures, integrating predictive modelling, real-time monitoring, and interactive alert dissemination to improve disaster preparedness and response herewith predictive modelling, real-time monitoring, and distributed alerts to foster disaster preparedness and response. Using machine learning models such as Long Short-Term Memory networks and Random Forest classifiers, Aegis predicts disaster events utilizing real-time weather, seismic, and environmental data. Aegis visualizes events on an interactive map, sends location alerts, generates AI-generated evacuation and rescue plans via SMS, and tracks time since the warning event. Aegis also conveys the UNDRR's early warning system core. Aegis encompasses hazard monitoring, hazard forecasting, risk communication, and preparedness steps Although there is considerable global advancement on EWS, many developing nations are still trying to establish adequate multi-hazard EWS with their limited resources and technology. Aegis is designed to address this gap by providing a scalable predictive machine learning platform for advancing proactive disaster mitigation, rapid response or informed recovery.

2.4 Community-Centric EWS and Human-Social Barriers.

A systematic review of 669 articles (37 selected using PRISMA) highlights human factors—socio-cultural constraints, community involvement, political instability, and communication challenges—as key barriers to effective early warnings [4]. The analysis Bangladesh’s EWS success story, emphasizing that community-based systems significantly enhance preparedness in flood and cyclone prone regions.

The research identifies lack of suitable monitoring equipment and inconsistent dissemination channels as major gaps.

2.5 Machine Learning Models for Flood Prediction and Crisis Response Flood centric research uses LSTM networks to analyze the historical rainfall, humidity, and temperature data for early flood prediction [2]. Models outperform traditional methods in detecting complex patterns in the meteorological data. AI enabled crisis-management systems provide real-time alerts, evacuation support, and post-disaster recovery assistance including relief camp identification and resource allocation.

These models demonstrate strong scalability for future multi hazard integration.

2. LITERATURE REVIEW 2.1 Machine Learning for Extreme Weather Forecasting Mukeshkumar et al. propose an Extreme Weather Prediction and Alert System that integrates Random Forest for feature selection and LSTM for time-series forecasting [1]. The system uses historical meteorological datasets and real-time API data to predict storms, heavy rainfall, and heatwaves.

Their hybrid ML model achieves improved accuracy by combining Random Forest’s capability to handle multidimensional weather parameters with LSTM’s

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2.6 AI-Based Disaster Prediction and Early Warning Frameworks New AI-based prediction frameworks emphasize GIS, remote sensing, IoT sensors, and predictive analytics in improving

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hazard alerts and warnings. These new systems are being developed to leverage multi-source environmental data input and machine learning classification models to estimate risk probabilities. While these systems and methods are more technically advanced than those previously used in EWS implementation, the paper indicates that publicly-available warnings are often still ineffective due to inconsistencies in data, lack of communication, and low public awareness of EWS procedures that limits warnings. An important step that the paper discusses is the potential use of more mobile alerting mechanisms, as well as adaptive decision-support tools, which may help to lower barriers to EWS adoption within communities.

systems lack fully automated response planning and operational plans for communities or decision-maker guidelines in dosing clear, actionable guidance. All of the aforementioned points represent potential line of extended research opportunity points for implementing a comprehensive AI-based prediction and alert systems dedicated to multi-hazard alerts. Aegis - Real-time Disaster & Weather Alert System directly addresses the need by integrating multiple advanced state-of-the-art ML/DL prediction model outputs, integrating geospatial intelligence information, utilizing real-time sensor streams, and utilizing AI build evacuation strategies all into one single integrated end-to-end framework; developed to increase accuracy, effectiveness, and disaster resilience.

2.7 Comparative Analysis of Machine Learning Models for Disaster Forecasting

3. SYSTEM OVERVIEW AND METHODOLOGY

A thorough examination of different ML models, including SVM, KNN, Random Forest, Naïve Bayes, and Neural Networks, illustrates differences in their success in predicting disaster scenarios. This assessment shows that hybrid and ensemble models are more successful than single-pronged algorithm models because they take into account the complex interactions that meteorological variables produce.

Nevertheless, several systems investigated do not predict in real time, and many cannot process sparse or noisy datasets for rapid evolution of disasters. These findings highlight the necessity of robust data and sensor integration strategies [7].

Aegis, which means ‘shield’ in Greek, is a disaster and weather alerting system that provides alerts staffed in realtime. It was designed with the objective of predicting, detecting, and communicating critical risk information that stems from AI enabled analytics. Aegis utilizes meteorological APIs, satellite derived indices, historical disaster history, and geospatial intelligence to provide customized, timely and precise early warning alerts. Flood disaster management involves the application of various methods that help lessen the impacts of floods on the environment. Some of the ac vi es in flood management include flood prevention, preparedness, response, and recovery. The strategy is designed to ensure safety from the adverse effects of floods in humans and property. This strategy needs to be used in conjunction with other methods that help inform the masses about the existence of floods.

These approaches use technological innovations to identify the regions prone to floods. They ensure that people can make preparations for floods to reduce the negative effects of floods. These strategies are essential because they help communities make preparations for floods. They collect information using sensors and satellite technology. It would then be possible for these communities to take action to mitigate any risks of flooding and prepare for floods. In addition, the development of reliable and sustainable flood warning systems requires the integration of approaches such as hydrological modelling, geospatial techniques, and community participatory approaches. This kind of approach not only improves the reliability and predictability of the model in forecasting floods but also provides information that can be understood easily by the local communities. The use of this multi-dimensional approach makes it easier for researchers to translate technical data into simple and understandable messages that help communities to take action to avoid being caught up in floods. It also makes it possible to improve the accuracy of flood forecasts through machine learning algorithms. The system combines meteorological APIs, satellite-derived indicators, historical disaster datasets, and geospatial intelligence to provide speedy, accurate, and personalized early warnings. Aegis has a front end built with React, and a back end built using the

2.8 Personalized AI-Driven Disaster Warning Systems New disaster alert systems seek to rely on artificial intelligence using user locations, proximity to threats, and other environmental considerations provide relevant personalized early warnings based on the user context.

Research has shown that personalized alerts trigger greater action by users compared to generic alerts. This approach requires advanced real-time analytics, robust edge processing architecture, and a dependable communication infrastructure [8]. There are considerable challenges in all hazard data privacy, achieving low-latency during network congestion, and developing adaptive models of operation in environment and emergency response multi-hazard settings.

2.9 Gaps and research opportunities (motivation for Aegis) While substantial advancements have been made regarding AI-supported prediction of what natural hazard may occur in the future, most existing systems are still very much in a transitional state and focused on one hazard such flood, earthquake, or storm. In addition, the absence of a single unifying multi-hazard approach contributes to delayed warnings, fragmented data sources, and limited situational awareness as these events transpire. New technologies such as machine learning/deep learning algorithms have the potential to deliver real-time alerts, but practical implementations tend to lean towards delivering "generic alerts" that provide little to no relevance regarding the unique geographic statistical risks or fractional environment perspective. Finally, many of the existing early warning

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Flask framework on Python. It provides dynamic hazard visualization through Leaflet.js, location-based risk evaluation, and automated evacuation strategy generation using Generative AI. Aegis overcomes the shortcomings of disparate tools for early-warning by providing prediction, detection, alerting, and planning in a single architectural framework.

3.1 System Architecture The proposed Aegis framework follows a modular, layered architecture designed for scalability, extensibility, and low latency processing. The architecture consists of six core layers: • This layer collects real-time data from multiple inputs, including weather APIs (Open Weather Map), seismic feeds (USGS), satellite-based climate indicators, and usergenerated location information. Environmental parameters such as temperature, humidity, rainfall, wind speed, and atmospheric pressure are continuously collected for prediction and monitoring tasks.

• The collected data is cleaned, normalized, and organized to be model-ready. The data is cleaned for noise, handled for missing values, and improved through feature extraction (e.g., rainfall intensity, pressure anomalies) to improve the accuracy of the model. For geospatial data, the coordinates and elevation metrics were structured into features of disaster-risk.

• Machine Learning and Deep Learning techniques predict occurrences, including floods, storms, and heat events. The earthquake risk estimator employs a hybrid approach by employing seismic trends along with patterns in the environment. The risk evaluation engine assesses and assigns severity-level scores by factoring in population density, vulnerability indicators, and proximity to hazard zones.

• Live environmental and seismic updates are visualized using Leaflet.js. Aegis highlights affected zones, displays hazard intensity gradients, and updates maps dynamically.

The system continuously scans incoming data to detect abnormal patterns indicative of developing disasters.

• The alert module issues instant warnings via SMS, push notifications, and email. Alerts are personalized based on the user’s GPS coordinates and predicted risk category. The system supports multi-channel redundancy to ensure message delivery even under network congestion. The complete system architecture is illustrated in figure 1.

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Fig -1: Aegis System Architecture 3.2 Workflow The operational workflow of Aegis proceeds through the following stages: 1. Data Collection: The system gathers realtime and historical data from satellites, IMD, weather APIs, and seismic feeds. These multi-source datasets provide the foundation for hazard prediction. 2. Data Processing: Raw data is cleaned, normalized, and structured to remove noise and missing values. Processed data is stored for efficient model usage. 3. Prediction Engine: AI/ML models analyze processed data to forecast hazards, determine probability, and estimate potential severity. Outputs are forwarded for risk evaluation. 4. Risk Analysis & Decision: Predictions are evaluated using severity thresholds and vulnerability criteria. The module decides whether the system should issue alerts or only store results. 5. Prediction Database: All analyzed predictions and risk results are stored for user display, tracking, and future model improvement. This database supports real-time access by the interface. 6. UserInterface: Users access predictions, maps, alerts, and safety information through the UI. It interacts with both prediction data and user profiles for personalization. 7. User Database: Stores user location, contact details, and preferences.

Enables hyper-local alerts and smooth communication between users and the system. 8. Risk & Response System: Alerts are dispatched via SMS, WhatsApp, email, or notifications based on risk level. Users receive real-time warnings and can send back status updates. 9. Continuous Learning: Feedback, real incident data, and user corrections are fed into the system to improve model accuracy. Updated models strengthen the prediction engine over time. This holistic workflow ensures that Aegis addresses all phases of the disaster lifecycle, from early prediction to long-term recovery planning.

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Table -1: Technological Modules Used

potential for high performance in real-world multi-hazard scenarios. Prior studies indicate that traditional early warning systems relying on single communication channels— primarily SMS or app notifications—achieve only 60–72% message reach during crisis events due to infrastructure failures, device inactivity, or lack of redundancy [1],[4]. These limitations highlight the vulnerability of legacy alerting systems, especially during high-impact disasters where communication networks degrade rapidly. Aegis addresses these challenges through a multi-channel alert dissemination strategy, delivering warnings through SMS, WhatsApp, email, and in-app notifications simultaneously. Literature shows that multimodal systems improve acknowledgment rates significantly compared to single-path alerting, particularly in regions with unstable connectivity [2],[5]. Figure 2 illustrates the comparative performance of different alert mechanisms, demonstrating that Aegis redundancy-based approach ensures the highest message delivery success under varying network conditions.

Fig -2: Alert delivery performance comparison, showing the advantage of multi-channel communication.

Alert delivery performance comparison, showing the advantage of multi-channel communication addition, previous research highlights the effectiveness of AI-driven forecasting models particularly LSTM architectures for flood and rainfall prediction, outperforming traditional statistical or threshold-based techniques due to their ability to capture long-term temporal dependencies [9]. The Aegis prototype corroborates these findings, as its LSTM-based flood module shows greater stability in detecting early hydrological anomalies compared to classical models. For wildfire and heatwave forecasting, remote-sensing-based studies have demonstrated the usefulness of multi-source data fusion using satellite imagery, vegetation indices, and meteorological indicators [10]. Aegis incorporates similar multi-variable fusion, enhancing its ability to distinguish between normal environmental fluctuations and early-stage hazard signatures.

4. RESULTS AND DISCUSSION Although large-scale deployment and public user testing of Aegis have not yet been conducted, evaluation of the functional prototype and system architecture reveals strong

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For earthquake-related risk classification, full prediction remains inherently uncertain, but anomaly-detection and

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hybrid seismological models have shown meaningful improvement in short-term hazard assessment [11]. Aegis adopts this approach by combining historical seismic data, micro-tremor observations, and secondary environmental parameters, enabling early-stage risk categorization rather than binary predictions. This aligns with global trends toward probabilistic, AI-supported frameworks. earthquake preparedness From a user experience standpoint, previous research shows that geospatial visualization significantly improves situational awareness during emergencies by allowing users to interpret risk zones more intuitively than textual warnings alone [6], [9].

The Leaflet-based mapping engine in Aegis reflects these findings, providing real-time hazard overlays, intensity gradients, and safe-zone routes that reduce cognitive load and facilitate quicker user response.

Although empirical field testing remains part of future work, the current design choices in Aegis AI-based multi-hazard prediction, redundant alert channels, geospatial visualization, and a feedback-driven continuous learning loop are well-aligned with proven strategies reported in the literature. Based on this convergence of research-backed design principles and prototype evaluations, Aegis is expected to deliver higher reliability, faster alert propagation, and improved disaster preparedness compared to conventional early-warning systems.

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6. CONCLUSION

5. CHALLENGES AND FUTURE WORK

Aegis is believed to be a game-changing technology that revolutionized disaster management and early warning technologies, addressing a number of shortcomings associated with existing disaster management techniques. One of the most significant challenges associated with conventional technology involves the use of fixed thresholds, late warnings, and limited communication options. It will be quite easy for the proposed approach to address the challenge due to its uniqueness, including the presence of AI. The Aegis solution is designed to work seamlessly with complex environmental data generated from diverse sources. The platform integrates environmental hazard data based on the analysis of weather data, geospatial data, and historical data from previous disasters. This capability allows the platform to continuously monitor the environment while dynamically adjusting the level of risks. While other solutions generate general notifications, Aegis concentrates on delivering specialized, situational alerts for every user. The advantage of using Aegis is that it employs machine learning models such as LSTM-based models for predicting outcomes. Using machine learning models enables identifying relationships across the time-series data obtained from the environment. This leads to the ability to forecast disasters like earthquakes, flooding, and heatwaves using the instrument. Anomaly detection can also be done using the Aegis tool, and this helps to detect anomalies that signal the upcoming disaster.

Thirdly, the capability of Aegis to provide immediate

Despite strong initial results, several challenges remain:  Data Inconsistency Across Sources: Real-time feeds from IMD, satellite systems, and weather APIs differ in accuracy, frequency, and resolution, making synchronized data fusion difficult.  Earthquake Predictability Limitations: Due to the inherently unpredictable nature of seismic events, only short-term risk classification is possible, not precise forecasting.  Network Dependency During Disasters: Severe events may cause connectivity failures, reducing the reliability of single-channel or even multi-channel alert delivery in remote regions.  High Computational Requirements: Running LSTM, hybrid ML ensembles, and continuous learning modules in real time increases server load and requires strong computational resources.  Limited Ground Truth Availability: Real-world validation data is often delayed, incomplete, or unavailable, slowing down model improvement and feedback loops.

Building on insights from existing literature and early testing, several enhancements are planned:  Integration of Advanced Remote Sensing: Incorporate SAR, NDVI, multispectral imagery, soilmoisture datasets, and high-resolution satellite inputs for enhanced multi-hazard prediction.

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Edge-Based Early Warning Devices: Deploy small, low-power edge prediction nodes to ensure alerts continue even during network outages or infrastructure damage.

Expansion to More Hazard Types: Extend the system to include landslides, cloudbursts, lightning prediction, air-quality hazards, and wildfire smoke dispersion tracking.

Crowdsourced and Community Data: Use user feedback, drone footage, citizen sensing, and social media signals to improve situational awareness and model accuracy.

Large-Scale User Trials: Conduct field tests with communities, disaster response teams, and government agencies for evaluating real-world performance.

Government and Institutional Integration: Collaborate with NDMA, SDMA, IMD, and local authorities to enable official alert channels and unified disaster-management pipelines.

Explainable AI (XAI) Development: Add interpretability modules to explain predict on reasoning, increasing system transparency and stakeholder trust.

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responses to the inputs is an essential feature. To achieve this purpose, Aegis has been designed in such a manner that the incoming information stream can be processed continuously. It becomes even more essential in case of natural disasters since delays may lead to dire consequences. The use of cloud technology allows for maintaining good performance while processing data streams. Good communication is essential to make an early warning system work effectively. The Aegis system does this part very well as it employs various communication methods that allow alerting through multiple channels. These include SMS, WhatsApp, email, among others. Through this strategy, the message gets to the user from various sides and hence increases its effectiveness since there may be times when one communication channel cannot be used, especially in cases where there could be a network failure. In addition to this, user participation and access will be another important factor determining the success of the system. In the coming years, certain aspects of the system can be improved in such a way that the system becomes more accessible, taking into consideration the issue of language, voice alerts, and the use of userfriendly interfaces. These aspects should be considered for people who are relatively vulnerable in society. Aegis is a comprehensive, integrated, AI-based multi-hazard disaster and weather alert framework that mitigates the shortcomings of conventional early-warning systems.

Through machine learning models, real-time data streams, geospatial visualization, and multi-channel delivery of alerts, Aegis is able to offer an effective and timely method for disaster preparedness. The architectural evaluation shows that Aegis can cleanly collect, process, and analyze heterogeneous environmental data sources while dynamically updating risk levels and delivering personalized alerts to end users. By putting together different ways to communicate with people like SMS, WhatsApp, email, and notifications in the app we can improve the reach and reliability of messages, especially during network outages. Additionally, the LSTM based forecasting module and anomaly-detection features give users a better means of forecasting floods, heat waves, and seismic hazards, than methods based on thresholding.

Preparatory visualizations, such as maps, and contextbased guidance for rescues, also help users with their situational awareness and readiness to respond. While future work is related to large-scale field application, the implementation plan of the system is inspired by validated findings from the original literature. There is a strong potential for Aegis to be an effective decision management tool for individuals, communities, and emergency management organizations. In the future, inclusion of remote sensing, edge-based alert nodes, and extended hazard coverage along with additional features may allow Aegis to be an integrated and adaptable platform which exceeds current progress towards improving individual, community- and broader

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hazard-related resilience from natural hazards.

Consequently, it should be stressed that Aegis is an innovative disaster management tool that applies innovative technologies while staying user-friendly. As such, Aegis can be considered a valuable resource that can deal with all the difficulties presented by the problem in question. Although there is room for improvement, adaptability and scalability of the system are likely to guarantee its future success. It can be suggested that the proper implementation of the Aegis system is likely to make our society more secure. Due to the fact that it employs both communication technologies and intelligent techniques, it can become a valuable tool to enhance disaster risk management. In this way, people and their property can be protected due to Aegis’ innovative qualities.

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[3] A. M. Nandi and B. Das, “Crisis-Connect: A Machine Learning-Based Flood Prediction and Disaster Response Framework Using LSTM Networks,” Journal of Water and Climate Change, 2023.

[4] J. Pescaroli and D. Alexander, “Understanding Compound, Cascading, and Interconnected Risks,” Frontiers in Communication, vol. 8, 2023.

[5] S. P. Singh and R. Kumar, “Disaster Prediction System Using Machine Learning,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 14, no. 3, 2025.

[6] K. Hossain and J. Rahim, “AI-Based Disaster Prediction and Early Warning Systems: A Review,” ResearchGate Preprint, 2024.

[7] A. Gupta and S. Verma, “Machine Learning Models for Natural Disaster Prediction: A Comprehensive Review,” International Journal of Research Publication and Reviews, vol. 6, issue 4, 2025.

[8] M. Q. Liu et al., “AI-Driven Personalized Disaster Warning and Risk Communication Systems,” ACM International Conference on Intelligent Computing, 2024.

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[9] N. R. Velu et al., “A Real-Time Disaster Monitoring System Using IoT, Cloud, and Deep Learning,” IEEE Access, vol. 9, pp. 78567–78578, 2021. Doi: 10.1109/ACCESS.2021.9430985. [10] D. Malzahn et al., “Wildfire Danger Prediction Using Remote Sensing and Meteorological Data,” Fires, vol. 6, no. 5, p. 192, MDPI, 2023.

[11] G. Zhang, Y. Li, and F. Ni, “Multi-Source Remote Sensing for Rapid Disaster Assessment and Prediction,” Remote Sensing, vol. 14, no. 13, p. 3159, MDPI, 2022.

[12] R. Ghali, M.A. Akhloufi, Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction. Fire (2023), Available at: https://www.mdpi.com/25716255/6/5/192

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