Crisis Connect: Real-time Emergency Response System using Machine Learning

Abstract

The \"Crisis-Connect\" project addresses the urgent need for effective disaster management solutions in the face of increasing natural and man-made disasters. By leveraging an extensive sensor network, the project collects real-time data on environmental parameters such as humidity, temperature, and ground movement. This data is analyzed using advanced machine learning models, including Random Forest and Support Vector Machines (SVMs), to accurately predict the likelihood of disasters like floods, forest fires, and earthquakes. The early detection and prediction capabilities of \"Crisis-Connect\" enable proactive emergency responses, facilitating timely evacuations and resource deployment to high-risk areas. In addition to predictive analytics, \"Crisis-Connect\" enhances coordination and information sharing during rescue operations, a critical need in the chaotic aftermath of disasters. Sensor data and victim locations are securely stored on cloud-based platforms, making them readily accessible to emergency teams. This system allows for informed decision-making and efficient management of rescue efforts. The project\'s user-friendly interfaces cater to both general users and emergency departments, providing incident information, initiating alerts, and coordinating response strategies. This integrated approach ensures swift and effective action from all stakeholders involved in crisis management. By combining geospatial technology, intelligent automation, and real-time detection algorithms, \"Crisis-Connect\" revolutionizes emergency response systems. Advanced mapping capabilities offer precise visualization of affected areas, aiding in the strategic allocation of resources and personnel. The seamless integration with service providers enhances the coordination and effectiveness of emergency response efforts. Overall, \"Crisis-Connect\" represents a comprehensive approach to disaster management, significantly improving crisis preparedness and response through technological innovation. As disasters become more frequent and severe, \"Crisis-Connect\" will be instrumental in mitigating their impact and ensuring community resilience.

Introduction

Project Overview

The “Crisis-Connect” initiative aims to improve disaster management by addressing two core challenges:

1. Early Detection and Prediction of Disasters

   • Uses real-time environmental data from IoT sensors (e.g., humidity, temperature, ground movement).

   • Employs machine learning models like Random Forest and Support Vector Machines (SVMs) to predict events such as floods, forest fires, and earthquakes.

   • Enables early warning systems for proactive emergency response.

2. Efficient Coordination During Rescue Operations

   • Secure cloud storage of sensor data and victim locations.

   • Provides user-friendly platforms for general users and emergency teams.

   • Enhances communication and coordination for faster and more effective disaster response.

II. Problem Statement & Literature Review

Current disaster management systems are largely manual, outdated, and reactive, leading to:

• Delayed responses

• Inaccurate risk identification

• Poor coordination during crises

Relevant Literature:

• Studies highlight the increasing urban flooding in India, challenges of IoT integration, and successful prototypes using IoT and cloud platforms for disaster prediction and response.

• Projects like smart fire systems, IoT-enabled flood detection, and community-based IoT networks demonstrate the potential of technology-driven disaster management.

III. Objectives

• Use real-time data, ML, IoT, and automation to:

  • Detect risks early

  • Improve emergency response

  • Enable smart resource allocation

  • Enhance coordination across stakeholders

IV. Existing vs. Proposed Systems

• Existing Systems: Rely on historical data and manual processes, lacking real-time prediction and integration.

• Proposed System ("Crisis-Connect"):

  • Integrates geospatial tech, intelligent automation, ML, and IoT.

  • Bridges the gap between disaster detection and response.

  • Offers centralized control, real-time analysis, and predictive alerts.

V. Methodology

System Components:

• IoT Devices: Include sensors (DHT11), GPS module (NEO06MV2), and ESP8266 Wi-Fi module.

• Cloud Platform: Data is uploaded (e.g., to ThingSpeak) for real-time monitoring.

• ML Model:

  • Uses unsupervised learning (due to lack of labeled data) with Python and sklearn.

  • Predicts a Risk Factor from preprocessed sensor and location data.

VI. Results & Discussion

• SVM Model for Earthquake Prediction:

  • Input features: Axis x, y, z from sensor data

  • Trained using SVC(kernel='linear')

  • Evaluated via:

    • Accuracy

    • F1 Score

    • Confusion Matrix

  • Demonstrates high performance in classifying seismic events, outperforming traditional methods.

Conclusion

Disasters, whether natural or man-made, inflict significant damage to property and result in the loss of lives. Effective disaster management is essential in mitigating these impacts and ensuring the safety and resilience of communities. Disaster management encompasses proactive planning to identify potential hazards, implementing measures to prevent or minimize their effects, and developing strategies for rapid response and recovery. This comprehensive approach is encapsulated in a disaster management plan, which outlines the risks faced by a business, preventive measures, and protocols for response and recovery. In conclusion, a well-structured disaster management plan serves as a crucial tool for businesses and communities, providing a framework for preparedness, response, and continuity in the face of adversity. It emphasizes the importance of proactive measures, clear communication, and collaborative efforts in safeguarding lives and livelihoods during times of crisis.

References

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Copyright

Copyright © 2025 Harshitha PM, Smitha Shekar B, Harish G. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.