http://lwf.ncdc.noaa.gov/oa/ncdc.html

U.S. Department of Interior, Dams, projects and power plants, Bureau of Reclamation. http://www.usbr.gov/dataweb/html/cbt.html#general

Federal Emergency Management Agency, Floodplain Information.

http://www.fema.gov

Ward Systems Group, Inc.

http://www.wardsystems.com

Northern Colorado Conservation District

http://www.ncwcd.org/project_features/boundary_map.asp

References

(HEC), H. E. C. (2000). Hydrologic modeling system HEC-HMS:  Technical reference manual. In U. S. A. C. o. Engineers (Ed.): U. S.  Army Corps of Engineers, Davis, California.

Agency, F. E. M. (2003). Clearing Up Flood Insurance Myths. Retrieved April 18, 2006, 2003, from http://www.fema.gov/news/newsrelease.fema?id=3681

Aussem, A., Murtagh, E., & M., S. (1995). Dynamical recurrent neural networks-Towards environmental time series prediction. International Journal of Neural Systems, 6, 145-170.

Baker, T. (2001). Assistant Director, Disaster Mitigation. Washington D. C.

Bazartseren, B., Hildebrandt, G., & Holz, K. P. (2003). Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing, 55(3-4), 439-450.

Blankert, R. L. (1994). Cloud Classification of AVHRR Imagery in Maritime regions using a Probabilistic Neural Network. Journal of Applied Meteorology, 33, 909-918.

Boznar, M., M., L., & Mlakar, P. (1993). A Neural Network-Based Method for Short-term Predictions of Ambient SO2 Concentrations in Highly Polluted Industrial Areas of Complex Terrain. Atmospheric Environment, 27B, 221-230.

Bruen, M., & Yang, J. (2005). Functional networks in real-time flood forecasting--a novel application. Advances in Water Resources, 28(92), 899-909.

Capodaglio, A. G., Jones, H. V., Novotny, V., & Feng, X. (1991). Sludge Bulking analysis and forecasting:  Application of system identification and artificial neural computing technology

Water Research, 25(10), 1217-1224.

Castellano-Mendez, M., Gonzalez-Manteiga, W., Febrero-Bande, M., Prada-Sanchez, J. M., & Lozano-Calderon, R. (2004). Modeling of the monthly and daily behavior of the runoff of the Xallas river using Box-Jenkins and neural network methods. Journal of Hydrology, 296(1-4), 38-58.

Chen, J., & Adams, B. J. (2006). Integration of artificial neural networks with conceptual models in rainfall-runoff modeling. Journal of Hydrology, 318(1-4), 232-249.

Choy, K. Y., & Chan, C. W. (2003). Modelling of river discharges and rainfall using radial basis function networks based on suppor vector regression. International Journal of Systems Science, 34(14-15), 763-773.

Dandy, G., & Maier, H. (1993, June, 1993). Use of Artificial Neural Networks for Forecasting Water Quality. Paper presented at the Stochastic and Statistical Methods in Hydrology and Environmental Engineering,  An International Conference in Honor of Professor T. E. Unny, Waterloo, Ontario, Canada.

Dawson, C. W., Abrahart, R. J., Shamseldin, A. Y., & Wilby, R. L. (2006). Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology, 319(1-4), 391-409.

Ekert, P., Cattani, D., & Ambuhl, J. (1996). Classification of Ensemble Forecasts by Means of an Artificial Neural Network. Meteorology Applications, 3, 169-178.

Filho, A. J. P., & dos Santos, C. C. (2006). Modeling a densely urbanized watershed with an artificial neural network, weather radar and telemetric data. Journal of Hydrology, 317(1-2), 31-48.

French, M. N., Krajewski, F. W., & Cuykendall, R. R. (1992). Rainfall forecasting in space and time using a neural network. Journal of Hydrology, 137, 1-31.

Hall, T., & Brooks, H. E. (1999). Precipitation Forecasting Using a Neural Network. Weather Forecasting, 14(3), 338-345.

Hertz, J., Krogh, A., & Palmer, R. (1991). Introduction to the Theory of Neural Computation: Addison-Wesley Publishing Company.

Hjelmfelt, T. A. J., & Wang, M. (1993). Artificial Neural Networks as Unit Hydrograph Applications. Paper presented at the Proceedings of the Symposium on Engineering Hydrology, ASCE, San Francisco.

Hsu, K. L., Grupta, H. V., & Sorooshian, S. (1996). A superior training strategy for three-layer feed forward artificial neural networks (Technique report No. HWR no. 96-030). Tucson: University of Arizona, Department of Hydrology and Water Resources.

Hsu, K. L., Gupta, V., & Sorooshian, S. (1995). Artificial neural network modeling of the rainfall-runoff process. Water Resources Research, 31, 2517-2530.

Huffman, W. S. (2001). Geographic Information Systems, Expert Systems and Neural Networks:  Disaster planning, mitigation and recovery, River Basin Management. Cardiff, Wales: WIT Press.

Hyndman, D., & Hyndman, D. (2006, April 21, 2006). Natural Hazards and Disasters. Retrieved August 19, 2006, 2006

Jia, X. (2000). IntelliGIS:  Tool for Representing and Reasoning Spatial Knowledge. Journal of Computing in Civil Engineering, 14(1), p51.

Jin, L., Gupta, M. M., & Nikiforuk, P. N. (1994). Computational Neural Architectures for Control Applications. New York: Prentice-Hall.

Jingyi, Z., & Hall, M. J. (2004). Regional flood frequency analysis for the Gan-Ming River basin in China. Journal of Hydrology, 296(1-4), 98-117.

Johnson, C. A., Yung, A. C., Nixon, K. R., & Legates, D. R. (2002). The Use of HEC-GeoHMS and HEC-HMS to Perform Grid-based Hydrologic Analysis of a Watershed, Proceedings of the 25th Annual Conference of the Association of State Floodplain Managers. Charlotte, North Carolina: The Association of State Floodplain Managers, Inc.

Kerh, T., & Lee, C. S. (2006). Neural networks forecasting of flood discharge at an unmeasured station using river upstream information. Advances in Engineering Software, Article in Press, Corrected Proof.

Kohonen, T. (1988). An Introduction to Neural Computing. Neural Networks(1), 3-16.

Krzysztofowicz, R. (2000). Hydrologic uncertainty processor for probabilistic river stage forecasting. Water Resources Research, 36(11), 3265-3277.

Lee, Y. H., & Singh, V. P. (1999). Tank Model Using Kalman Filter. Journal of Hydrologic Engineering, 4(4), 344-349.

Markus, M. (1997). Application of neural networks in stream flow forecasting. Unpublished Doctoral, Colorado State University, Fort Collins.

Marzban, C., & Stumpf, G. J. (1996). A Neural Network for Tornado Prediction based on Doppler Radar-Derived Attributes. Journal of Applied Meteorology, 35, 617-626.

Mashudi, M. R. (2001). Forecasting Water Demand Using Neural networks in the Operation of Reservoirs in Citarum Cascade, West java, Indonesia. Unpublished Dissertation, The George Washington University.

Mazouz, K. (2003). Neural Networks: Nova Southeastern University, Huizenga School of Entrepreneurship.

Mazouz, K. (2006). Multiple Regression Analysis of the Big Thompson River (pp. 3). Boca Raton: Florida Atlantic University.

McCann, D. W. (1992). A Neural Network Short-Term Forecast of Significant Thunderstorms. Weather Forecasting, 7, 525-534.

McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biology, 5, 115-133.

Muller, B., & Reinhardt, J. (1990). Neural Networks, an Introduction: Springler-Verlag.

NCDC. (2006). Gaging Station Identification Data. Retrieved August 19, 2006, 2006

NCWCD (Cartographer). (2005). West NCWCD Boundary Map

Neary, V. S., Habib, E., & Fleming, M. (2004). Hydrologic modeling with NEXRAD precipitation in middle Tennessee. Journal of Hydrologic Engineering, 9(5), 339-349.

Rajurkar, M. P., Kothyari, U. C., & Chaube, U. C. (2004). Modeling of the daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology, 285(1-4), 96-113.

Ramsey, C. (2003). Field Agent, Disaster Recovery, Federal Emergency Management Agency. Denver, Colorado.

Sahoo, G. B., & Ray, C. (2006). Flow forecasting for a Hawaii stream using rating curves and neural networks. Journal of Hydrology, 317(1-2), 63-80.

Sahoo, G. B., Ray, C., & De Carlo, E. H. (2005). Use of neural network to predict flash flood and attendant water qualities of a mountainous stream on Oahu, Hawaii. Journal of Hydrology, Article in Press, Corrected Proof.

Schultz, G. A. (1996). Remote sensing applications to hydrology:  Runoff. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 41(4), 453-475.

See, L., & Abrahart, R. J. (2001). Multi-model data fusion for hydrological forecasting. Computers & Geosciences, 27(8), 987-994.

USGS, U. S. G. S. (2006a). USGS 06738000 BIG THOMPSON R AT MOUTH OF CANYON, NR DRAKE, CO. Retrieved November 14, 2006, 2006

USGS, U. S. G. S. (2006b, November 14, 2006). USGS 06741510 BIG THOMPSON RIVER AT LOVELAND, CO. Retrieved November 14, 2006, 2006, from http://waterdata.usgs.gov/co/nwis/inventory/?site_no=06741510

USGS, U. S. G. S., & Inc, M. a. a. c. (2006). Topographic map of the Big Thompson Canyon. Retrieved November 14, 2006, 2006, from http://www.topozone.com/map.asp?lat=40.41559&lon=-105.223&size=l&u=4&datum=nad27&layer=DRG

Vieux, B. E., & Bedient, P. B. (2004). Assessing urban hydrologic prediction accuracy through event reconstruction. Journal of Hydrology, 299(3-4), 217-236.

Wang, K., & Zaniewski, J. (1995, 1995). Computing Methods for Transportation Management Systems. Paper presented at the Transportation Congress Proceedings, New York, New York.

Ward Systems Group, I. (2000). NeuroShell Predictor Retrieved July 2001, 2001, from http://www.wardsystems.com

Wei, Y., Xu, W., Fan, Y., & Tasi, H.-T. (2002). Artificial neural network based predictive method for flood disaster. Computers & Industrial Engineering, 42(204), 383-390.

Zhang, S. P., Watanabe, H., & Yamada, R. (1993, June, 1993). Prediction of Daily Water Demands by Neural Networks. Paper presented at the Stochastic and Statistical Methods in Hydrology and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada.

Zhu, M., & Fujita, M. (1993, June, 1993). Application of Neural Networks to Runoff Forecast. Paper presented at the Stochastic and Statistical Methods in Hydrology and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada.