Using Big Data to Optimally Model Hydrology and Water Quality across Expansive Regions


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by Edwin A. Roehl, Jr., Advanced Data Mining International, LLC, 3620 Pelham Rd. PMB 351, Greenville, SC 29615, ed.roehl@advdmi.com,
John B. Cook, Advanced Data Mining International, LLC, 3620 Pelham Rd. PMB 351, Greenville, SC 29615, john.cook@advdmi.com,
Paul A. Conrads, U.S. Geological Survey South Carolina Water Science Center, Stephenson Center. Suite 129, 720 Gracern Rd., Columbia, SC 29210, pconrads@usgs.gov,



Document Type: Proceeding Paper

Part of: World Environmental and Water Resources Congress 2009: Great Rivers

Abstract: This paper describes a new divide and conquer approach that leverages big environmental data, utilizing all available categorical and time-series data without subjectivity, to empirically model hydrologic and water-quality behaviors across expansive regions. The approach decomposes large, intractable problems into smaller ones that are optimally solved; decomposes complex signals into behavioral components that are easier to model with sub-models; and employs a sequence of numerically optimizing algorithms that include time-series clustering, nonlinear, multivariate sensitivity analysis and predictive modeling using multi-layer perceptron artificial neural networks, and classification for selecting the best sub-models to make predictions at new sites. This approach has many advantages over traditional modeling approaches, including being faster and less expensive, more comprehensive in its use of available data, and more accurate in representing a system's physical processes. This paper describes the application of the approach to model groundwater levels in Florida, stream temperatures across Western Oregon and Wisconsin, and water depths in the Florida Everglades.

Subject Headings: Data processing | Hydrologic models | Water quality | Hydrologic data | Optimization models | Numerical models | Sensitivity analysis | Data analysis | Time series analysis | North America | United States | Florida | Wisconsin | Oregon

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