Camera-Based Intelligent Stream Stage Sensing for Decentralized Environmental Monitoring

Abstract

On average, flood damages cost $4.4 billion in the US annually. Accurate, vast, and real-time coverage of water level monitoring is crucial for the advancement of environmental research, specifically in the areas of climate change, water distribution, and natural disaster preparedness and management. According to a 2018 EPA report, there are 2.7 million streams and associated watersheds in the US with an inadequate monitoring network of just 8,300 sensors. Hence, the current state of water monitoring requires an immediate solution to produce low-cost and accurate water level measurement sensors. This research presents a novel methodology for intelligent stream stage measurement that utilizes prevalent sensors commonly found in smart devices. The methodology creates a distinct opportunity for a low-cost camera-based embedded system that will measure water levels and share surveys to support environmental monitoring and decision-making. Presented intelligent stage sensing is implemented as an in-situ installation of a complete single-board computer (i.e., a stand-alone sensor), which utilizes a registry of structures and points of interest (POI) along with the core modules of the application logic: (1) deep-learning powered water segmentation module and (2) geometric POI calculation module. The implementation relies on a Raspberry Pi with a motorized camera for automated measurements and is supported by a PID controller and multiprocessing. For future work, the involvement of the camera supports further use cases such as recognizing objects (e.g., debris, trees, humans, boats) on the water surface using artificial intelligence and image processing. In addition, the method shown can be made into a progressive web application (PWA) that can be used on smartphones to allow crowdsourced citizen science applications for environmental monitoring.