Application of Deep Learning for Imaging‐Based Stream Gaging
Ryan L. Vanden Boomen, Zeyun Yu, Qian Liao
Abstract
Abstract Stream gages are critically important for measuring stream flow in water resources management. Stream gages monitor and record flow and water stage within some water body. The United States Geological Survey maintains a network of stream gages across the country. Many of these sites are also equipped with webcams to gather real‐time river information, especially in flood flows. Remotely measuring flow discharge using particle image tracking has been researched intensively. This study demonstrates a process for training a deep neural network that will utilize the webcam and stream gage data to generate a water stage prediction based on webcam captured river images. This study presents the experiments on stream gages located at the Clear Creek in Iowa, Auglaize River in Ohio, and Milwaukee River in Wisconsin. The process outlined utilizes transfer learning and well‐known image classification models as a basis for a generalized river stage regression model. Across the training, validation, and deployment experiments, the developed process shows success in creating an accurate model for these testing sites of different perspective settings. The results of this study show confidence in future studies utilizing ground‐level remote stream gaging with Machine Learning‐enabled image regression.