Physics-guided recurrent neural networks for predicting lake water temperature
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Abstract
This chapter presents a physics-guided recurrent neural network model (PGRNN) for predicting water temperature in lake systems. Standard machine learning (ML) methods, especially deep learning models, often require a large amount of labeled training samples, which are often not available in scientific problems due to the substantial human labor and material costs associated with data collection. ML models have found tremendous success in several commercial applications, e.g., computer vision and natural language processing. The chapter presents PGRNN as a general framework for modeling physical processes in engineering and environmental systems. The proposed PGRNN explicitly incorporates physical laws such as energy conservation or mass conservation. In particular, researchers started pursing this direction by using residual modeling, where an ML model is learned to predict the errors, or residuals, made by a physics-based model. Advanced ML models, especially deep learning models, often require a large amount of training data for tuning model parameters.
| Publication type | Book chapter |
|---|---|
| Publication Subtype | Book Chapter |
| Title | Physics-guided recurrent neural networks for predicting lake water temperature |
| Chapter | 16 |
| DOI | 10.1201/9781003143376-16 |
| Year Published | 2022 |
| Language | English |
| Publisher | Taylor & Francis |
| Contributing office(s) | WMA - Integrated Information Dissemination Division |
| Description | 26 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Monograph |
| Larger Work Title | Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data |
| First page | 373 |
| Last page | 398 |
| Google Analytic Metrics | Metrics page |