Deploying the Power of Machine Learning and Near Real Time Modeling for Stage Forecasting in Streams
Forecasting stream flows and flood depths is an essential task in flood management and water resource planning. Hydrologic/hydraulic simulation models or data-driven approaches can be used to generate these real-time predictions. Modern advances in artificial intelligence and state-of-the-art machine learning (ML) techniques have provided new opportunities for quicker streamflow prediction processes with more efficient and accurate forecasts.
In this presentation, we will dig into the use of ML to develop flow depth prediction models. Prediction models using different ML algorithms and various prediction settings are trained and validated based on the historic precipitation data and stream flow depth observations. These models apply different ML techniques, including multiple linear regression, support vector regression, random forest regression, artificial neural network multi-layer perceptron, long short-term memory and ensemble learning.
Case examples will illustrate how radar and forecasted rainfall datasets can be applied to traditional H&H models and ML models to provide lead times of several hours or days in advance of a flood event. These forecasts can be critical for issuing flood warnings and the real-time operation of flood control systems.
Author Bio
Steve Godfrey is a modeling team leader with over 25 years of experience in solving stormwater problems for local municipalities and state agencies across the country. He specializes in using hydrologic and hydraulic modeling for developing stormwater master plans, developing design alternatives to improve flood management, and working with near real time stream forecasting.
Dr. Arash Karimzadeh is a Hydraulic and Hydrologic Modeling Engineer with more than 20 years of experience in multi-disciplinary firms, providing research studies and engineering services in water resources management projects. He has a Ph.D. in Infrastructure and Environmental Systems from the University of North Carolina at Charlotte as well as a Master and a Bachelor of Science in Civil Engineering. Arash focuses on utilizing hydraulic and hydrologic analysis to develop master plans for improving stormwater and sanitary sewer systems and designing flood management plans. In addition, he is an expert in utilizing data analysis and machine learning techniques in different fields.
