International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 255-262, January 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
G. V. Mahidhar Reddy
Syed Mahammad Saleem
R. Nikhitha Sharma
T. Dhanush
B. Sumanth Kumar
Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.
Abstract: Floods remain one of the most devastating natural disasters, causing significant loss of life, infrastructure damage, and environmental degradation, particularly in remote and resource-constrained regions. Conventional flood monitoring systems are largely reactive, rely on limited sensing parameters, and often fail during extreme weather due to power and communication constraints. This paper presents an AI-driven, LoRaWAN-enabled, solar-powered multi-sensor framework designed for proactive flood prediction and early warning. The proposed system integrates rainfall, water level, flow rate, atmospheric pressure, temperature, and humidity sensors with an embedded controller for real-time environmental monitoring. Sensor data are transmitted over long distances using low-power LoRaWAN communication and uploaded to a cloud platform via an IoT gateway. Python-based machine learning models analyze historical and real-time data to predict flood risk levels in advance. Alert mechanisms include GSM-based SMS notifications and local alarms through visual and audio indicators. Solar energy with battery backup ensures uninterrupted operation during power outages. Experimental results demonstrate reliable long-range communication, continuous monitoring, and timely alert generation, making the proposed framework suitable for deployment in flood-prone and remote regions to enhance disaster preparedness and response.
Keywords: Flood prediction, LoRaWAN, multi-sensor monitoring, solar power, early warning systems.
References:
- M. Sathiyamoorthy and P. Subramanian, “AI-Driven Early Warning System for Predicting and Mitigating Urban Flooding: A Comparative Analysis of Real-Time Data and Predictive Algorithms,” 2025 2nd International Conference on Computing and Data Science (ICCDS), Chennai, India, 2025, pp. 1-6, doi: 10.1109/ICCDS64403.2025.11209450.
- T. Duan, P. Li, P. Huang, and W. Wei, “Fast urban flood modeling informing response decisions: Model development and future perspectives,” Advanced Engineering Informatics, vol. 70, p. 104152, Dec. 2025, doi: 10.1016/j.aei.2025.104152.
- S. M and S. P, “AI-Driven Early Warning System for Predicting and Mitigating Urban Flooding: A Comparative Analysis of Real-Time Data and Predictive Algorithms for Enhanced Flood Management,” 2025 2nd International Conference on Computing and Data Science (ICCDS), Chennai, India, 2025, pp. 1-6, doi: 10.1109/ICCDS64403.2025.11209748.
- L. -C. Chang, M.-T. Yang, and F.-J. Chang, “Flood resilience through hybrid deep learning: Advanced forecasting for Taipei’s urban drainage system,” Journal of Environmental Management, vol. 379, p. 124835, Mar. 2025, doi: 10.1016/j.jenvman.2025.124835.
- T. Q. Dang et al., “Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management,” Environmental Modelling & Software, vol. 183, p. 106246, Oct. 2024, doi: 10.1016/j.envsoft.2024.106246.
- P. M. Ranieri et al., “Water level identification with laser sensors, inertial units, and machine learning,” Engineering Applications of Artificial Intelligence, vol. 127, p. 107235, Oct. 2023, doi: 10.1016/j.engappai.2023.107235.
- P. M. Rasalkar and A. R. Surve, “Evolution of Flood Prediction Methods: Hydrological, Statistical, Machine Learning and Deep Learning Approaches (2020–2025),” 2025 International Conference on Future Technologies (ICFT), Sangli, India, 2025, pp. 1-6, doi: 10.1109/ICFT66708.2025.11336611.
- P. Charan, M. M. Siddiqui, V. Yadav, C. Pathak, Y. Narayan and Z. H. Khan, “AI-Enhanced Early Warning Systems for Natural Disaster Detection and Mitigation using Wireless Sensor Networks,” 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES), Lucknow, India, 2024, pp. 1-6, doi: 10.1109/IC3TES62412.2024.10877562.
- D. Yu, Q. Tao, Q. Liu, Y. Jin, Y. Sun, and P. Fu, “Lifecycle management of urban renewal enabled by Internet of Things: Development, application, and challenges,” Results in Engineering, vol. 27, p. 105706, Jun. 2025, doi: 10.1016/j.rineng.2025.105706.
- M. Dash, C. Parida, M. Pandey, A. Dash, G. Bhuyan and S. Darshana, “AI-Driven Climate Disaster Prediction Using ConvLSTM and GANs,” 2025 International Conference on Artificial intelligence and Emerging Technologies (ICAIET), Bhubaneswar, India, 2025, pp. 1-6, doi: 10.1109/ICAIET65052.2025.11211377.
- S. B. Bhonde, H. S. Chattar, S. S. Gawande, K. S. Fulsoundar and H. S. Bharitkar, “Modeling and Forecasting River Flood Inundation Using Machine Learning,” 2025 9th International Conference on Computing, Communication, Control and Automation (ICCCBEA), Pune, India, 2025, pp. 1-6, doi: 10.1109/ICCUBEA65967.2025.11283912.
- J. Ali et al., “A deep dive into cybersecurity solutions for AI-driven IoT-enabled smart cities in advanced communication networks,” Computer Communications, vol. 229, p. 108000, Nov. 2024, doi: 10.1016/j.comcom.2024.108000.