Login | Register

Design and Implementation of an IoT Platform for Flood Prediction


Design and Implementation of an IoT Platform for Flood Prediction

Mehta, Meet Divyesh (2023) Design and Implementation of an IoT Platform for Flood Prediction. Masters thesis, Concordia University.

[thumbnail of Mehta_MCompSc_S2023.pdf]
Text (application/pdf)
Mehta_MCompSc_S2023.pdf - Accepted Version
Available under License Spectrum Terms of Access.


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.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Mehta, Meet Divyesh
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:4 August 2023
Thesis Supervisor(s):Jaumard, Brigitte and Glatard, Tristan
Keywords:Internet of Things (IoT), MQTT protocol, data collection, centralized monitoring, real-time analytics, online machine learning, flood prediction, water level forecasting.
ID Code:992650
Deposited By: Meet Divyesh Mehta
Deposited On:14 Nov 2023 20:37
Last Modified:14 Nov 2023 20:37
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top