Numerically optimized empirical modeling of highly dynamic, spatially expansive, and behaviorally heterogeneous hydrologic systems - Part 1

Proceedings of the iEMSs 3rd Biennial Meeting," Summit on Environmental Modelling and Software"

Volumn , Issue , 2006, Pages

  Roehl, Edwin ^a   Risley, John ^b   Stewart, Jana ^b   Mitro, Matthew ^c  

^a Advanced Data Mining   (United States)

^b U S GEOLOGICAL SURVEY   (United States)

^c Wisconsin DNR   (United States)

Author keywords

Classification; Clustering; Model; Neural network

Indexed keywords

ALTERNATIVE APPROACH; CALIBRATION AND VALIDATIONS; CATEGORICAL VARIABLES; CLUSTERING; DATA MINING ALGORITHM; DYNAMIC BEHAVIORS; EMPIRICAL MODELING; END-USER APPLICATIONS; FINITE ELEMENT; FLOW MODEL; FORCING FUNCTION; HYDROLOGIC SYSTEMS; MODELING APPROACH; MULTI LAYER PERCEPTRON; MULTI-STEP; MULTIPLE DATA TYPES; NATURAL SYSTEMS; PERIODIC BEHAVIOR; PREDICTION MODEL; SIGNAL DECOMPOSITION; SITE TO SITES; STREAM TEMPERATURES; TIME SERIES CLUSTERING;

CLASSIFICATION (OF INFORMATION); DATA MINING; MATHEMATICAL MODELS; MODELS; NEURAL NETWORKS; OPTIMIZATION; TIME SERIES; WATER LEVELS;

FINITE ELEMENT METHOD;

EID: 84858697422     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Conrads, P.A., and E.A. Roehl, Comparing physics-based and neural network models for predicting salinity, water temperature, and dissolved-oxygen concentration in a complex tidally affected river basin, paper presented at the South Carolina Environmental Conference, Myrtle Beach, March 15-16, 1999.
2. Conrads, P.A., E.A. Roehl, and W.P. Martello, Development of an empirical model of a complex, tidally affected river using artificial neural networks," Water Environment Federation TMDL Specialty Conference, Chicago, Illinois, November 2003.
3. Dowla, F.U. and L.L. Rogers, Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks, MIT Press, 159-172, Cambridge MA, 1996.
4. Coppola, E.A., A.J. Rana, M.M. Poulton, F. Szidarovszky, and V.W. Uhl, A neural network model for predicting aquifer water level elevations, Ground Water 43(2), 231-241, 2005.
5. Jensen, B.A., Expert systems - neural networks, Instrument Engineers' Handbook Third Edition, Chilton, Radnor PA, 1994.
6. Risley, J.C., E.A. Roehl and P.A. Conrads, Estimating water temperatures in small streams in western Oregon using neural network models, U.S. Geological Survey Water-Resources Investigations Report 02-4218, 2003.