Flooding, a major natural calamity, severely threatens communities and infrastructures in areas susceptible to floods. Consequently, implementing an Internet of Things (IoT)-based flood monitoring system becomes crucial. Existing flood monitoring systems lack a comprehensive and scalable IoT platform to collect real-time data from diverse sensors efficiently, visualize flood information, and provide accurate water level forecasts. This thesis proposes a complete system designed to address the challenges associated with efficient data collection and flood monitoring from diverse IoT sensors. 

Our proposition involves creating and deploying a centralized system known as HYDROSIGHT, which facilitates the real-time gathering, monitoring, and visualization of flooding-related sensor data. HYDROSIGHT system also provides a log monitoring feature for effective debugging and troubleshooting.
The IoT environment for flood monitoring and prediction system was designed to promote sustainability and autonomy by preferring sensors with minimal footprints and compatibility with solar panels. The system architecture leverages a 4G network for seamless data transmission.

To validate the practical applicability of the proposed design,HYDROSIGHT system was tested at two municipalities of Quebec, namely Terrebonne, and Lac-Supérieur. In addition, the platform was also deployed at the Ericsson facility in Montreal to test the 5G capabilities. The deployment in these locations allowed us to evaluate the performance and effectiveness of the HYDROSIGHT system in a real flood monitoring environment.

In addition to implementing the IoT testbed, a preliminary machine learning tool was developed on water level forecasting. In this experiment, we opted for an online machine-learning approach, recognizing the significance of real-time updates and low computational resources of IoT devices.  Leveraging the constantly updating data from HYDROSIGHT, we trained and tested our online machine-learning model, enhancing its forecasting capabilities.

We conducted a comparative analysis to understand the advantages of online machine learning over traditional batch learning. This analysis involved examining the water level forecasting results obtained from both methods using time series data from the HYDROSIGHT system deployed at Lac-Supérieur in Quebec.