An Ensemble Approach Using Recurrent Dynamic Artificial Neural Network Models to Forecast Net Inflow to Lake Okeechobee, Florida
Publication: World Environmental and Water Resources Congress 2024
ABSTRACT
Accurate hydrologic forecasts can inform and improve water management decisions. This paper presents the development of an ensemble forecasting approach using a set of recurrent dynamic artificial neural network models having the Nonlinear AutoRegessive with eXogenous input (NARX) architecture. The many decisions that were made during the model-development process are summarized. Application of the k-fold cross-validation and ensemble methodology to forecasting net inflow to Lake Okeechobee, Florida, is exhibited. The ensemble forecast method is demonstrated to provide better generalization performance and more accurate forecasts than can be provided by a single NARX model.
Get full access to this chapter
View all available purchase options and get full access to this chapter.
REFERENCES
Ali, A., Neidrauer, C., Cadavid, L., Linton, P., Tarboton, K., and Obeysekera, J. (2006). New rainfall-based Management Plan for more Natural Water Deliveries to the Everglades Shark River Slough, Operating Reservoirs in Changing Conditions. In: Proceedings of the Operations Management 2006 Conference, August14–16, 2006, Sacramento, CA.
Ali, A. (2009). “Nonlinear multivariate rainfall–stage model for large wetland systems”. Journal of Hydrology 374 (2009) 338–350.
Choromanska, A., Henaff, M., Mathieu, M., Ben Arous, G., and LeCun, Y. (2015). “The Loss Surfaces of Multilayer Networks.” Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 38:192-204. Available from https://proceedings.mlr.press/v38/choromanska15.html.
ECMWF (European Centre for Medium-Range Weather Forecasts). (2023). https://confluence.ecmwf.int/display/CKB/ECMWF+model+and+products.
Krogh, A., and Vedelsby, J. (1994). “Neural Network Ensembles, Cross Validation and Active Learning.” Proceedings of the 7th International Conference on Neural Information Processing Systems, 231–238.
Levenberg, K. (1944). “A Method for the Solution of Certain Non-Linear Problems in Least Squares”. Quarterly of Applied Mathematics. 2 (2): 164–168. doi:https://doi.org/10.1090/qam/10666.
MATLAB. Neural Network Toolbox Release R2019b (2019), The MathWorks, Inc., Natick, Massachusetts, United States.
Marquardt, D. (1963). “An Algorithm for Least-Squares Estimation of Nonlinear Parameters”. SIAM Journal on Applied Mathematics. 11 (2): 431–441. doi:https://doi.org/10.1137/0111030. hdl:10338.dmlcz/104299.
National Oceanic and Atmospheric Administration, and WPC (Weather Prediction Center). Quantitative Precipitation Forecasts (2023). https://www.wpc.ncep.noaa.gov/qpf/qpf2.shtml.
Neidrauer, C. (2024). Assessing Hydrologic Model Fitness and Visualizing Multi-Objective Performance: The Standard Fitness Index and the Fitness Index Target Diagram, [Manuscript submitted for publication with Water Resources Research].
Nash, J. E., and Sutcliffe, J. V. (1970). “River Flow Forecasting Through Conceptual Models. Part 1 – A Discussion of Principles.” Journal of Hydrology, 10, 282–290.
Prechelt, L. (2012). “Early Stopping — But When?” In: Montavon, G., Orr, G.B., Müller, KR. (eds) Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science, vol 7700. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35289-8_5.
SFWMD (South Florida Water Management District). Quantitative Precipitation Forecasts (2023). https://www.sfwmd.gov/weather-radar/sfwmd-forecast.
USACE. (1999). Central and Southern Florida Project Comprehensive Review Study. Final Integrated Feasibility Report and Programmatic Environmental Impact Statement (PEIS). https://usace.contentdm.oclc.org/digital/collection/p16021coll7/id/11296/rec/2.
USACE. (2008). Central and Southern Florida Project, Water Control Plan for Lake Okeechobee and Everglades Agricultural Area. Jacksonville District. March 2008. https://www.saj.usace.army.mil/Portals/44/docs/h2omgmt/LORSdocs/2008_LORS_WCP_mar2008.pdf.
Information & Authors
Information
Published In
World Environmental and Water Resources Congress 2024
Pages: 1 - 15
History
Published online: May 16, 2024
ASCE Technical Topics:
- Artificial intelligence (AI)
- Artificial intelligence and machine learning
- Bodies of water (by type)
- Computer programming
- Computing in civil engineering
- Continuum mechanics
- Dynamic models
- Engineering fundamentals
- Engineering mechanics
- Flow (fluid dynamics)
- Fluid dynamics
- Fluid mechanics
- Forecasting
- Hydraulic engineering
- Hydrologic engineering
- Hydrologic models
- Inflow
- Lakes
- Mathematics
- Model accuracy
- Models (by type)
- Neural networks
- River engineering
- Rivers and streams
- Statistics
- Water and water resources
- Water management
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.