Surface-water quality and flow Modeling Interest Group

Neural Network Model Reading List

This list is sorted alphabetically, by first author's last name. Help us maintain this list by sending additions or corrections to sarounds@usgs.gov.

Links to FAQs and Archives:

Battelle Pacific Northwest Labs: Neural network model freeware and shareware, URL: http://www.emsl.pnl.gov:2080/proj/neuron/neural/systems/shareware.html

Sarle, W.S., ed., 1999, Neural Network FAQ, part 1 of 7: Introduction, periodic posting to the Usenet newsgroup comp.ai.neural-nets, URL: ftp://ftp.sas.com/pub/neural/FAQ.html

Sarle, W.S., ed., 1999, Neural Network FAQ, part 4 of 7: Books, data, etc., periodic posting to the Usenet newsgroup comp.ai.neural-nets, URL: ftp://ftp.sas.com/pub/neural/FAQ4.html

Sarle, W.S., ed., 1999, Neural Network FAQ, part 5 of 7: Free software, periodic posting to the Usenet newsgroup comp.ai.neural-nets, URL: ftp://ftp.sas.com/pub/neural/FAQ5.html

Sarle, W.S., ed., 1999, Neural Network FAQ, part 6 of 7: Commercial software, periodic posting to the Usenet newsgroup comp.ai.neural-nets, URL: ftp://ftp.sas.com/pub/neural/FAQ6.html
 

Beale, R. and Jackson, T., 1990, Neural Computing, an Introduction, Adam Hilger, IOP Publishing Ltd: Bristol.

Hinton, G.E., 1989, Connectionist learning procedures, Artificial Intelligence, 40, 185-234.

Hinton, G.E., 1992, How neural networks learn from experience, Scientific American, 267 (September), 144-151.

Jensen, B.A., 1994, Expert systems - neural networks, Instrument Engineers' Handbook, 3rd ed.: Chilton, Radnor, PA, 48-54.

Kohonen, T., 1988, An introduction to neural computing, Neural Networks, 1(1), 3-16.

Rumelhart, D.E., Hinton, G.E., and Williams, R.J., 1986, Learning representations by back-propagating errors, Nature, 323 (9 October), 533-536.
 

Olmsted, David D., History and principles of neural networks to 1960
[online]

Olmsted, David D., History and principles of neural networks from 1960 to present
[online]
 

Braddock, R.D., Kremmer, M.L., Sanzogni, L., 1998, Feed-forward artificial neural network model for forecasting rainfall run-off, Environmetrics, 9(4), 419-432.

Brion, G.M., Neelakantan, T., and Lingireddy, S., 2000, Using neural networks to predict peak cryptosporidium concentrations, J American Water Works Association, 93(1), 99-105.

Brion, G.M., and Lingireddy, S., 1999, A neural network approach to identifying non-point sources of microbial contamination, Water Research, 33(14), 3099-3106.

Clair, T.A., J.M. Ehrman, 1998, Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers, Water Resources Research, 34(3), 447-455.

Conrads, P.A. and Roehl, E.A., 1999, Comparing physics-based and neural network models for simulating salinity, temperature, and dissolved-oxygen in a complex, tidally affected river basin, in Proceedings of the 1999 South Carolina Environmental Conference, March 15-16, 1999, Myrtle Beach, SC.
[full text online]

Conrads, P.A., Roehl, E.A., Cook, J.B., 2002, Estimation of tidal marsh loading effects in a complex estuary: in Coastal Water Resources, J.R. Lesnik (ed.), AWRA 2002 Spring Specialty Conference Proceedings, TPS-02-1, pp. 307-312.
[full text online]

Conrads, P.A., Roehl, E.A. Jr., Martello, W.P., 2002, Estimating point-source impacts on the Beaufort River using artificial neural network models: in Coastal Water Resources, J.R. Lesnik (ed.), AWRA 2002 Spring Specialty Conference Proceedings, TPS-02-1, pp. 289-294.
[full text online]

Coulibaly, P., Anctil, F., Rasmussen, P., Bobee, B., 2000, A recurrent neural networks approach using indices of low-frequency climatic variability to forecast regional annual runoff, Hydrological Processes, 14(15), 2755-2777.
[ http://www3.interscience.wiley.com:80/cgi-bin/jissue/75503150]

Crespo, J.L. and Mora, E., 1993, Drought estimation with neural networks, Advances in Engineering Software, 18.

Dowla, F., Leach, R., Glenn, L., Moran, B., Heinle, R., 1993, Neural network methods in hydrodynamic yield estimation, Pure and Applied Geophysics, 140(3), 427-454.

Elshorbagy, A. and Simonovic, S.P., 2000, Performance evaluation of artificial neural networks for runoff prediction, J. Hydrologic Engineering, 5(4), 424-427.
[Abstract]

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

French, M., Recknagel,F., and Jarrett, G.L., 1998, Scaling issues in artificial neural network modeling and forecasting of algal bloom dynamics, in Proceedings of the International Water Resources Engineering Conference, 1, 891-896.

Grubert, J.P., 1995, Application of neural networks in stratified flow stability analysis, Journal of Hydraulic Engineering, 121(7), 523-532.

Hsu, K., Gupta, H.V., and Sorooshian, S., 1995, Artificial neural network modeling of the rainfall-runoff process, Water Resources Research, 31(10), 2517-2530.

Hsu, K., Gupta, H.V., and Sorooshian, S., 1998, Streamflow forecasting using artificial neural networks, in Proceedings of the International Water Resources Engineering Conference, 2, 967-972.

Jain, S.K. and Chalisgaonkar, D., 2000, Setting up stage-discharge relations using ANN, J. Hydrologic Engineering, 5(4), 428-433.
[Abstract]

Karunanithi, N., Grenney, W.J., Whitley, D., and Bovee, K., 1994, Neural networks for river flow predictions, Journal of Computing in Civil Engineering, 8(2), 201-218.

Lingireddy, S., 1998, Aquifer parameter estimation using genetic algorithms and neural networks, Civil Engineering and Environmental Systems, 15, 125-144.

Mason, J.C., Price, R.K., and Tem'me, A., 1996, A neural network model of rainfall-runoff using radial basis functions, Journal of Hydraulic Research, 34(4), 537.

Neelakantan, T.R., Lingireddy, S., and Brion, G.M., 2002, Relative performance of different ANN training algorithms in predicting protozoa concentration in surface waters, ASCE Journal of Environmental Engineering, 128(6), 533-542.

Neelakantan, T., Brion, G.M., and Lingireddy, S., 2001, Neural network modeling of cryptosporidium and giardia concentrations in the Delaware River, Water Science and Technology, 43(12), 125-132.

Poff, L.N., Tokar, S., and Johnson, P., 1996, Stream hydrological and ecological responses to climate change assessed with an artificial neural network, Limnology and Oceanography, 41(5), 857.

Ray, C. and Klindworth, K.K., 1998, Use of neural networks as a tool to assess pesticide contamination potential of rural domestic wells, in Proceedings of the International Water Resources Engineering Conference, 2, 973-978.

Risley, J.C., Roehl, E.A., and Conrads, P.A., 2002, Estimating water temperatures in small streams in western Oregon using neural network models: U.S. Geological Survey Water-Resources Investigations Report 02-4218.

Roehl, E.A. and Conrads, P.A., 1999, Real-time control for matching wastewater discharges to the assimilative capacity of a complex, tidally affected river basin, in Proceedings of the 1999 South Carolina Environmental Conference, March 15-16, 1999, Myrtle Beach, SC.
[full text online]

Roehl, E.A. and Conrads, P.A., 2000, Real-time control of the salt front in a complex, tidally affected river basin: in Proceedings of the ANNIE 2000 Conference, November 5-8, 2000, St. Louis, MO.
[full text online]

Rounds, S.A., 2002, Development of a neural network model for dissolved oxygen in the Tualatin River, Oregon: in Proceedings of the Second Federal Interagency Hydrologic Modeling Conference, Las Vegas, NV, July 29 - August 1, 2002: Subcommittee on Hydrology of the Interagency Advisory Committee on Water Information.
[full text online]

Sanchez, L. and others, 1998, Use of neural networks in design of coastal sewage systems, Journai of Hydraulic Engineering, 124(5), 457-464.

Shamseldin, A.Y., 1997, Application of a neural network technique to rainfall-runoff modelling, Journal of Hydrology, 199, 272-294.

Shamseldin, A.Y., O'Connor, K.M. and Liang, G.C., 1997, Methods for combining the output of different rainfall-runoff models, Journal of Hydrology, 197, 203-229.

Tangang, F.T. and others, 1998, Forecasting ENSO Events: A neural network-extended EOF approach, Journal of Climate, 11, 29-41.

Thirumalaiah, K., and M.C. Deo, 1998, River stage forecasting using artificial neural networks, Journal of Hydrologic Engineering, 26-32

Tingsanchali, T., Gautam, M.R., 2000, Application of tank, NAM, ARMA and neural network models to flood forecasting, Hydrological Processes, 14(14), 2473-2487.
[ http://www3.interscience.wiley.com:80/cgi-bin/jissue/75503124]

Walley, W.J., and V.N. Fontama, 1998, Neural network predictors of average score per taxon and number of families at unpolluted river sites in Great Britain, Water Research, 32(3).

Wen, C. and Lee, C., 1998, A neural network approach to multiobjective optimization for water quality management in a river basin. Water Resources Research, 34(3), 427.

Yang, C.C., Chen, C., and Chang, L., 1998, Modeling of watershed flood forecasting with time series artificial network algorithm, in Proceedings of the International Water Resources Engineering Conference, 1, 903-908.

Zhang, Q. and Stanley, S.J., 1997, Forecasting raw-water quality parameters for the North Saskatchewan River by neural network modeling, Water Research, 31(9), 2340.
 

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