Cloud-Based River Water Level Monitoring System

Project Description

Overview

This project developed a cloud-enabled water level monitoring and early-warning system designed to improve flood prediction and environmental safety.
By using high-resolution cameras installed at elevated points, including mountain regions and riverbanks, the system performs long-term visual surveillance of river water levels.
The data is continuously processed and stored on cloud servers, enabling real-time monitoring and historical analysis to support proactive flood prevention and infrastructure resilience.

Research Objectives

The primary goal of the project was to design a scalable, low-maintenance, and automated system capable of observing and predicting water level variations under diverse environmental conditions.
Key objectives included:

  • Implementing remote visual monitoring through long-range cameras in isolated or elevated terrains.

  • Integrating cloud-based analytics for real-time data storage, processing, and visualization.

  • Developing automated detection algorithms to identify critical water level thresholds and send alerts.

  • Supporting long-term environmental data collection for hydrological research and regional safety planning.

Technical Approach

The system integrates advanced sensing, computer vision, and cloud computing technologies:

  • Edge Video Capture – Cameras configured with solar-powered modules for 24/7 operation in remote areas.

  • Cloud Processing – Data transmitted via secure IoT channels to cloud servers for automated analysis.

  • AI-Based Water Level Estimation – Algorithms detect river boundaries and estimate level variations through time-series visual analytics.

  • Alert Mechanism – Automated notifications triggered when significant deviations or flood risks are detected.

Methodology Framework

  1. Site Selection – Installation of cameras at high vantage points ensuring wide coverage and visibility of river flows.

  2. Data Acquisition – Continuous capture of time-lapse imagery and environmental parameters.

  3. Cloud Integration – Transmission to scalable cloud infrastructure for analysis and long-term storage.

  4. Flood Risk Modeling – Use of predictive algorithms to forecast rapid water level rises and potential overflow.

  5. Public and Institutional Access – Authorized dashboards for local authorities and disaster management units.

Expected Outcomes

  • Improved early flood detection through continuous river monitoring.

  • Cloud-based data architecture enabling real-time access and predictive insights.

  • Reduced environmental risk and better protection of downstream communities.

  • Open framework adaptable to other geographic regions and environmental monitoring projects.

Research Areas Alignment

This project aligns with ongoing work in:

  • Environmental Informatics – Application of AI and IoT to ecological monitoring and sustainability.

  • Computer Vision for Infrastructure Safety – Real-time analysis of natural phenomena via video surveillance.

  • Cloud and Edge Computing – Distributed architectures for data-intensive monitoring systems.

  • Disaster Risk Management – Digital tools for anticipatory flood response and community protection.

Cloud-Based River Water Level Monitoring System