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Development of a Secured IoT-Based Flood Monitoring and Forecasting System Using Genetic-Algorithm-Based Neuro-Fuzzy

Hero Rafael Castillo Arante¹, Edwin Sybingco¹, Maria Antonette Roque¹

¹Department of Electronics and Computer Engineering, De La Salle University, 2401 Taft Avenue, Malate, Manila 1004, Philippines.

Sensors (Basel, Switzerland)

|July 12, 2025

Summary

This study developed a flood prediction system for the Philippines using a neuro-fuzzy LSTM network and genetic algorithm, achieving 92.91% accuracy. The system enhances community awareness and preparedness for floods, aiming to reduce damage and save lives.

Keywords:

LSTM flood prediction fuzzy inference system genetic algorithm

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Area of Science:

Environmental Science
Computer Science
Artificial Intelligence

Background:

Flooding poses a significant risk in the Philippines, causing property damage and loss of life.
Real-time flood status monitoring is crucial for enhancing community awareness and preparedness.

Purpose of the Study:

To develop and evaluate a secure flood prediction system for the Philippines.
To improve flood awareness and reduce the impact of flood events.

Main Methods:

A flood prediction model integrating fuzzy logic, Long Short-Term Memory (LSTM) neural networks, and genetic algorithms was developed.
A secure data flow was established using an Infineon security module for data transmission to AWS IoT Core, Timestream, and Grafana.
Model performance was compared using different configurations of LSTM hidden layers, epochs, and learning rates.

Main Results:

The neuro-fuzzy LSTM network with a genetic algorithm achieved a flood prediction accuracy of 92.91%.
The optimal model configuration involved an ADAM solver, predicting floods every 3 hours with specific learning rates and LSTM hidden layer parameters.
This approach demonstrated improved accuracy compared to a baseline ADAM solver prediction model.

Conclusions:

The developed flood prediction system significantly enhances prediction accuracy.
The integration of advanced algorithms and secure data transmission provides a reliable tool for flood management.
The system has the potential to increase flood awareness and preparedness, thereby mitigating flood-related damages and saving lives.