Forecasting water levels using machine (deep) learning to complement numerical modelling in the southern Everglades, USA
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Abstract
Water level is an important guide for water resource management and wetland ecosystems, defining one of the most basic processes in hydrology. This research seeks to investigate the possibility of complementing numerical modeling with a Machine Learning (ML) model to forecast daily water levels in the southern Everglades in Florida, USA. An exact analytical solution to water level may not be possible, but using the computational methods afforded by ML, the traditional numerical techniques may be enhanced to generate more robust, scalable predictions. Five locations were chosen for application of the Time-Delayed Neural Network (TDNN) and Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) ML models, which were built to estimate water level with 1, 2, 3, 7 and 10 day forecasts using a simulation step of 1 day. The results showed that rainfall forecasts from weather models could improve water-level forecasts if the accuracy and performance of the weather models can be improved. The ML models presented here improve water-level predictions from a historical hydrologic model for a 24 hour forecast horizon.
Study Area
| Publication type | Book chapter |
|---|---|
| Publication Subtype | Book Chapter |
| Title | Forecasting water levels using machine (deep) learning to complement numerical modelling in the southern Everglades, USA |
| Chapter | 7 |
| DOI | 10.1002/9781119639268.ch7 |
| Year Published | 2024 |
| Language | English |
| Publisher | American Geophysical Union |
| Contributing office(s) | Caribbean-Florida Water Science Center |
| Description | 35 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Monograph |
| Larger Work Title | Advanced hydroinformatics: Machine learning and optimization for water resources |
| First page | 177 |
| Last page | 211 |
| Country | United States |
| State | Florida |
| Other Geospatial | Everglades |
| Google Analytic Metrics | Metrics page |