Environmental AI-Based Multi-Sensor Disaster Early Warning System: A Physics-Integrated Research Report

Abstract

Environmental disasters such as floods, heatwaves, and air pollution episodes pose escalating threats to human health, safety, and economic stability across vulnerable communities worldwide. Traditional early warning systems typically rely on centralized, capital-intensive infrastructure with delayed response times and limited local adaptability. These systems often fail to capture micro-climatic variations and are inaccessible to remote and low-income communities. This research presents an integrated, affordable, AI-based Environmental Disaster Early Warning System that combines multiple environmental sensors (rainfall, temperature, humidity, air quality, vibration) with microcontroller platforms (Arduino, ESP32) and Python-based machine learning algorithms. The system classifies environmental conditions into three distinct risk categories—Normal, Warning, and Dangerous—using Bayesian decision logic, thresholding, and anomaly detection. Real-time multi-sensor data fusion enables rapid, localized risk assessment with minimal dependence on cloud infrastructure. The project deliberately integrates physics, computer science, and environmental science. From a physics perspective, the system exploits the principles of thermodynamics (heat transfer and heatwaves), fluid mechanics (runoff and flooding), atmospheric physics (air pollution dispersion), electronics (sensor operation), and the piezoelectric effect (vibration sensing). From a computational perspective, we implement a naïve Bayes classifier and rule-based thresholds to infer disaster likelihood. Laboratory and limited field testing demonstrate that the prototype system can detect hazardous conditions with an overall mean classification accuracy of approximately 88–90% across flood, heatwave, and severe air pollution scenarios, with a decision latency below 200 ms. Hardware cost per unit is in the range of ?3,500–?5,500, making community-scale deployment realistic. The modular design supports extension to additional hazards and integration with national disaster management platforms. This work illustrates how school-level, physics-grounded research can contribute to practical, low-cost early warning solutions aligned with Atmanirbhar Bharat and Viksit Bharat 2047.

Introduction

Natural disasters—floods, heatwaves, droughts, cyclones, and air pollution—cause significant human and economic losses globally and in India. Traditional early warning systems face challenges including high infrastructure costs, communication delays, low spatial resolution, limited access in remote areas, and reliance on cloud services. There is a need for affordable, decentralized, physics-grounded monitoring systems that communities can maintain locally.

Motivation and Innovation:
The project aims to develop a low-cost, modular system that:

• Senses environmental parameters using physics-based sensors.

• Applies AI (Bayesian classification) to interpret sensor data into risk levels.

• Operates offline with fast response (<200 ms) and affordability (~?3,500–?5,500/unit).

Key Innovations:

1. Multi-sensor data fusion: Monitors rainfall, temperature, humidity, air quality, and ground vibrations simultaneously for detecting compound events.

2. Bayesian risk classification: Uses naïve Bayes for probabilistic disaster state estimation.

3. Physics-integrated design: Sensor selection and interpretation based on fundamental principles (Ohm’s law, thermistor behavior, semiconductor gas sensing, piezoelectric effect).

4. Offline AI processing: Minimizes reliance on cloud or network connectivity.

5. Cost-effective and scalable: Suitable for schools, panchayats, and rural communities.

System Architecture:

• Sensor Layer: Rainfall (conductivity), temperature-humidity (DHT11/DHT22), air quality (MQ-135), piezoelectric vibration.

• Processing Layer: Arduino/ESP32 for data acquisition and preprocessing, connected to local Python computation.

• Decision and Alert Layer: Bayesian classifier with LED indicators and optional mechanical actuators.

Sensor Physics & Calibration:

• Rainfall sensor: Conductivity-based, voltage output proportional to wetness.

• Temp-humidity sensor: Thermistor and capacitive element; thresholds define normal, warning, dangerous.

• Air quality sensor (MQ-135): Gas-sensitive resistance changes indicate pollution levels.

• Piezo sensor: Measures ground vibrations via piezoelectric charge generation.

Data Processing:

• Sensors polled every 100 ms; moving averages, standard deviation, and rate-of-change features computed.

• Bayesian classification discretizes sensor readings and calculates probability of disaster states.

Implementation & Results:

• Prototype tested in lab (simulated conditions) and field (flood-prone semi-urban area).

• Achieved accuracy: floods ~89%, heatwaves ~87%, severe air pollution ~85%, compound events ~92%.

• Average decision latency <200 ms.

• Errors arose from sensor noise, simplified ground truth, naïve independence assumptions, and limited field deployment.

Physics Principles Applied:

• Thermodynamics: Urban heat islands and heatwaves measured via temperature and humidity sensors.

• Fluid mechanics: Flood risk inferred from rainfall intensity, humidity, and surface wetness.

• Atmospheric physics: Air pollution episodes inferred from gas sensor readings under stable atmospheric conditions.

• Electronics & piezoelectricity: Sensors operate based on voltage-current-resistance relationships and mechanical-to-electrical energy conversion.

Conclusion

This project demonstrates that a physics-informed, AI-assisted, multi-sensor platform can serve as a practical early warning tool for floods, heatwaves, and air pollution episodes in resource-constrained settings. By integrating principles from thermodynamics, fluid mechanics, atmospheric physics, electronics, and probability theory, the system achieves around 88–90% classification accuracy in test scenarios with decision latencies below 200 ms and a hardware cost under ?6,000. The work is suitable as a research report for Physics and interdisciplinary STEM, illustrating how theoretical concepts are directly applied in real-world problem solving. With further refinement and validation, such systems could complement national early warning infrastructures and empower local communities to respond proactively to environmental risks.

References

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Copyright

Copyright © 2025 Aadith Nair P, Indrathreedh V P, Adwaith Balamurali, Niranjan Subhash. 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.