Haqim Haslee

myFloodML

myFloodML is a cutting-edge flood management solution developed by the members Engineering Team Project at Universiti Teknologi PETRONAS. This innovative project employs a synergistic approach, combining IoT devices, cloud computing, and machine learning to address the critical challenge of flood prevention and mitigation

At the core of myFloodML lies a robust network of sensor-equipped devices, including ESP32-Cam and Arduino, strategically positioned along local riverbanks. These devices continuously monitor water levels, capturing real-time data that is crucial for informed decision-making. The collected data is seamlessly transmitted to a centralized cloud-based database, Firebase Firestore, where it is securely stored and managed for efficient analysis.

Leveraging the power of TensorFlow, a state-of-the-art machine learning framework, myFloodML develops sophisticated predictive models. By analyzing historical water level data and incorporating other relevant hydrological parameters, these models can accurately forecast potential flood events, providing valuable early warnings. This proactive approach empowers communities and emergency response agencies to prepare and respond effectively to flood threats.

Beyond flood prediction, myFloodML aims to create a user-friendly interface to disseminate timely flood alerts to stakeholders. This essential component ensures that critical information reaches those who need it most, facilitating timely evacuation, resource allocation, and damage prevention

By integrating IoT, cloud computing, and artificial intelligence, myFloodML offers a holistic approach to flood management. This project has the potential to significantly enhance flood resilience and minimize the impact of these devastating natural disasters.