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projects > greater everglades hydrology monitoring network: data mining and modeling to separate human and natural hydrologic dynamics
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Project Summary Sheet
Fiscal Year 2006 Study Summary Report
Project Title: Hydrology Monitoring Network: Data Mining and Modeling to Separate Human and Natural Hydrologic Dynamics
Associated / Linked Projects: Estimation of Critical Parameters in Conjunction with Monitoring of the Florida Snail Kite Population, The Everglades Depth Estimation Network (EDEN) for Support of Ecological and Biological Assessments, Freshwater Flows into Northeastern Florida Bay, Southern Inland and Coastal Systems (SICS) Model Development, Enhanced Water Quality Monitoring and Modeling Program for the A.R.M. Loxahatchee National Wildlife Refuge
Overview & Objective(s): The emerging field of Data Mining addresses the issue of extracting information from large databases. It is comprised of several technologies that include signal processing, advanced statistics, multi-dimensional visualization, machine learning (including artificial neural networks (ANN)), and Chaos Theory. Data Mining can solve complex problems that may be unsolvable by any other means. The data from the CERP monitoring is a tremendous resource for addressing the critical questions for restoring the South Florida ecosystem. Estuarine systems are difficult systems to analyze due to the complexity of environmental factors occurring simultaneously. To enhance the evaluation of the CERP data base, there is an immediate need to apply new methodologies to systematically analyze the data set to answer critical questions such as relative impacts of controlled freshwater releases, tidal dynamics, and meteorological forcing on streamflow, water level, and salinity. This project will directly address the data analysis issues outlined above.
The first year of the Data Mining Analysis Project addressed these issues by demonstrating how data mining techniques can be applied to the Everglades data bases and ecological studies. Three studies were selected for the demonstration work - Freshwater Inflows to Northeastern Florida Bay (Mark Zucker, Clinton Hittle), Estimation of Critical Parameters in Conjunction with Monitoring the Florida Snail Kite Population (Wiley Kitchens), and Southern Inland and Coastal Systems (Eric Swain). In addition, other projects were identified where data mining techniques can be applied during Years 2 and 3 of the project. In the second year of the Data Mining Analysis Project, additional projects included working with PI's involved with the Everglades Depth Estimation Network (Pamela Telis, Roy Sonenshein, John Jones, and Leonard Pearlstine) and beginning work on the analysis and modeling of the water level and water quality of the A.R.M. Loxahatchee National Wildlife Refuge (Laura Brandt and Mike Waldon).
Status: The hindcasted hydrology for 17 vegetation transects of the Snail Kite study in WCA-3a developed during Year 1 were incorporated into a Decision Support System (DSS) in Year 2. The Excel application incorporates the historical database, ANN hindcast models (including decorrelation models), model simulation controls, statistical output summaries, streaming graphics, and model output into a easily disseminated spreadsheet package. Output from regional hydrologic routing models (for example the 2 by 2 model) can be used as input to the DSS system to analyze water depth response at the Snail Kite vegetation transects. In Year 1, methodologies for estimating water levels and water depths at ungaged areas using ANN models were developed using a data set from WCA- 3A. The approach utilizes static variables of location and percent vegetation and dynamic variables of water levels at known locations. During Year 2, the application of the methodology to the EDEN network of the Greater Everglades was undertaken. The first step in scaling up the methodology was to cluster the water level data of the EDEN data into classes of similar behavior. The estimation methodology can then be applied to classes within compartments in the Everglades. The 6-year EDEN data set was clustered and preliminary models for estimating at ungaged sites have been developed for WCA-3a. ANN models of salinity response for the 5 USGS gaged tributaries to Florida bay were finalized using the 1996-2004 data. The models were used to analyze the influence of control releases and natural streamflow on the salinity dynamics of 5 tributaries to Florida Bay. In Year 2, we have begun working with water-level and water-quality datasets from the A.R.M. Loxahatchee National Wildlife Refuge to analyze the influence of operations schedule and control of high conductivity water intrusion into the Refuge. Data from 1996 to 2005 has been incorporated into a database for analysis and ANN model building.
Recent & Planned Products: Major products for Year 2 include (1) an Excel based DSS application for of the hindcasted hydrology for the Snail Kite study; (2); cluster analysis of 6-year EDEN database (3) ANN models used to analyze freshwater inflows for 5 tributaries into Florida Bay for natural and anthropogenic components; and (4) 10-year database of water level and water quality database for A.R.M. Loxahatchee National Wildlife Refuge.
Year 2 conference papers (peer reviewed), posters, and presentations:
Conrads, P.A. and Roehl, E.A., 2006, Estimating water depths using artificial neural networks, Hydroinformatics 2006, edited by Philippe Gourbesville, Jean Cunge, Vincent Guinot, Shie-Yui Liong, Vol. 3, p.1643-1650
Conrads, P.A. and Roehl, E.A., Daamen, R.C., and Kitchens, W.M., 2006, Using artificial neural network models to integrate hydrologic and ecological studies of the snail kite in the Everglades, USA, Hydroinformatics 2006, edited by Philippe Gourbesville, Jean Cunge, Vincent Guinot, Shie-Yui Liong, Vol. 3, p.1651-1658
Conrads, P.A. and Roehl, E.A., 2006, Analysis of the Process Physics of Tributaries to Florida Bay Using Artificial Neural Networks and Three-Dimensional Response Surfaces, Florida Bay and Adjacent Marine Systems Science Conference, Duck Key, Florida, Dec. 11-14, 2005
Conrads, P.A. and Roehl, E.A., 2006, Application of a Dynamic Clustering Algorithm to the Water-Level Hydrographs of the EDEN Hydrologic Network, Greater Everglades Ecosystem Restoration Conference, Orlando, Florida, June 5-9, 2006
Conrads, P.A. and Roehl, E.A., 2006, Estimating Water Depths at Ungaged Locations in the Florida Everglades Using Artificial Neural Networks, Greater Everglades Ecosystem Restoration Conference, Orlando, Florida, June 5-9, 2006
Paul A. Conrads, Ruby Daamen, Edwin A. Roehl, Wiley M. Kitchens, and Christa Zweig, 2006, Using Artificial Neural Network Models to Integrate Hydrologic and Ecological Studies of the Snail Kite Falcon in the Everglades, Greater Everglades Ecosystem Restoration Conference, Orlando, Florida, June 5-9, 2006
Specific Relevance to Information Needs Identified in DOI's Science Plan in Support of Ecosystem Restoration, Preservation, and Protection in South Florida (DOI's Everglades Science Plan): [Page numbers listed below are from the DOI Everglades Science Plan. The Science Plan is posted on SOFIA's Web site: http://sofia.usgs.gov/publications/reports/doi-science-plan/]:
U.S. Department of the Interior, U.S. Geological Survey
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Last updated: 04 September, 2013 @ 02:08 PM(KP)
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