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Spatial Analysis of Hydrologic and Environmental Data Based on Artificial Neural Networks

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Artificial Neural Networks in Hydrology

Part of the book series: Water Science and Technology Library ((WSTL,volume 36))

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Abstract

The analysis and modeling of the spatial variability associated with geophysical data such as hydrological and environmental data, have been of concern to geophysicists for many decades. For instance, the spatial characterization of rainfall, the variability of parameters describing groundwater flow such as transmissivity and hydraulic conductivity, the variation of water quality properties in a lake or reservoir, the spatial distribution of acid rainfall, and the spatial variability of droughts, are only a few examples.

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References

  • Abetew, W., J. obeysekera, and G. Shih, Spatial Analysis for Monthly Rainfall in South Florida, Water Resources Bulletin, 29(2), 179–188, 1993.

    Article  Google Scholar 

  • Ahmed, S., and G. De Marsily, Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity, Water Resour. Res., 23(9), 1987.

    Google Scholar 

  • Armstrong, M., A Cross-Classification of the Internal Reports on Geostatistics, Mathematical Geology, 14 (5), 109–537, 1982.

    Article  Google Scholar 

  • ASCE Task Committee on Geostatistical Techniques, Review of Geostatistics in Geohydrology: I. Basis Concepts, J. of Hydraulic Engineering, 116(5), 612–632, 1990a.

    Article  Google Scholar 

  • ASCE Task Committee on Geostatistical Techniques, Review of Geostatistics in Geohydrology: II. Applications, J. of Hydraulic Engineering, 116(5), 633–658, 1990b.

    Article  Google Scholar 

  • Bishop, C. M., Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.

    Google Scholar 

  • Bras, R. L, and I. Rodriguez-Iturbe, Random Functions and Hydrology, Addison-Wesley Pub. Comp., 1985.

    Google Scholar 

  • Burke, L. I., Introduction to Artificial Neural Systems for Pattern Recognition, Computers Ops. Res., 18(2), 211–220, 1991.

    Article  Google Scholar 

  • Cacoullos, T., Estimation of a multivariate density, Ann. Inst. Statist. Math. (Tokyo), 18(2), 179–189, 1966.

    Google Scholar 

  • Cain, J. B., An improved probabilistic neural network and its performance relative to other models, Application of Artificial Neural Networks, SPIE Vol. 1294, 1990.

    Google Scholar 

  • Chakraborty K., K. Mehrotra, C. K. Mohan, and S. Ranka, Forecasting the Behavior of Multivariate Time Series Using Neural Networks, Neural Networks, Vol. 5, 961–970, 1992.

    Article  Google Scholar 

  • Craun, L., and R. C. Knox, Evaluation of Septic Tank System Effects on Ground Water Quality, EPA-600/2–84-107, Washington, D. C., 1984.

    Google Scholar 

  • Delhomme, J. P., Spatial Variability and Uncertainty in Groundwater Flow Parameters: A Geostatistical Approach, Water Resour. Res., 15(2), 269–280, 1979.

    Article  Google Scholar 

  • Duda, R. O. and P. E. Hart, Pattern Classification and Scene Analysis, Wiley, NY, 1973.

    Google Scholar 

  • Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA., 1989.

    Google Scholar 

  • Haykin, S., Neural Networks: A Comprehensive Foundation, Macmillan College Pub.Comp., Inc., 1994.

    Google Scholar 

  • Hertz, J., A. K. Krogh, and R. G. Palmer, Introduction to the Theory of Neural Compution, Santa Fe Institute Studies in the Science ofComplexity, Vol 1., 1991.

    Google Scholar 

  • Hecht-Nelsen, R., Counterpropagation networks, Appl. Opt., 26(23), 1987.

    Google Scholar 

  • Hopfield, J. J., Neural Networks and Physical Systems with Emergent Collective Computational Abilities, Proc. Natl. Acad. Sci. U.S.A., 79, 2554–2558, 1982.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hughes, J. P. and D. P. Lettenmaier, Data Requirements for Kriging: Estimation and Network Design, Water Resour. Res., 17(6), 1641–1650, 1981.

    Article  Google Scholar 

  • Johansson, E. M., F. U. Dowla, and D. M. Goodman, Back-Propagation Learning for Multi-layer Feed-forward Neural Networks Using the Conjugate Gradient Method, Int. J. Neural Syst., 2(4), 291–301, 1992.

    Article  Google Scholar 

  • Journel, A. G., Geostatistics: Models and Tools for the Earth Science, Mathematical Geology, 18(1), 119–140, 1986.

    Article  Google Scholar 

  • Karunanithi, N., W. J. Grenney, D. Whiteley, and K. Bovee, Neural Network for River Flow Prediction, J. of Computing in Civil Engineering, 8(2), 1994.

    Google Scholar 

  • Kassim, A. H. M. and N. T. Kottegoda, Rainfall Network Design through Comparative Kriging Methods, Hydrological Sciences — Journal des Sciences Hydrologiques, 36(3), 1991.

    Google Scholar 

  • Kitanidis, P. K., Geostatistics, Handbook of Hydrology, 20.1 - 20.39, McGraw-Hill, 1993.

    Google Scholar 

  • Kohonen, T., Self-organized Formation of Topologically Correct Feature Maps, Biol. Cybern., 43, 59–69, 1982.

    Article  Google Scholar 

  • Maloney, P. S., Use of Probabilistic Neural Networks for Emitter Correlation, Applications ofArtificial Neural Networks, SPIE Vol. 1294, 1990.

    Google Scholar 

  • Markus, M., H. Shin, and J. D. Salas, Predicting Streamflows based on Neural Networks, 1995 First International Conference on Water Resources Engineering, ASCE, San Antonio, TX, 1995.

    Google Scholar 

  • Masters, T., Advanced Algorithms for Neural Networks : A C++ Source book, 158–191, John Wiley & Sons, Inc., 1994.

    Google Scholar 

  • Matheron, G., The theory of regionalized variables and its applications, Les Cahiers du Centre de Morphologie Mathematique de Fontainbleau, Fontainbleau, 1971.

    Google Scholar 

  • Matheron, G., Simple Substitute for Conditional Expectation: The Disjunctive Kriging, Proceedings, NATO A.S.I Geostat 1975, D. Reidel, Hingham, Mass, 1976.

    Google Scholar 

  • McDonnell, J. R., and D. Waagen, Evolving Recurrent Perceptrons for Time Series Modeling, IEEE Trans. on Neural Networks, 5(1), 1994.

    Google Scholar 

  • Moody, J. E., and D. J. Darken, Fast Learning in Networks of Locally-tuned Processing Units, Neural computation 1, 281–294, 1989.

    Article  Google Scholar 

  • Musavi, M. T., K. H. Chan, D. M. Hummels, K. Kalantri, and W. Ahmed, A probabilistic Model for Evaluation of Neural Networks Classifiers, Pattern Recognition, 25(10), 1241–1251, 1992.

    Article  Google Scholar 

  • Narendra, K. S., and K. Parthasarathy, Identification and control of dynamical systems using neural networks, IEE Trans. on Neural Networks, Vol. 1, 4–27, 1990.

    Article  Google Scholar 

  • Novotny, V., H. Jones, X. Feng, A. Capodaglio, Time Series Analysis Models of Activated Sludge Plants, Wat. Sci. Tech., Vol. 23, 1107–1116, 1991.

    Google Scholar 

  • Olea, R.A, Sampling Design Optimization for Spatial Functions, Mathematical Geology, 16(4),369–392, 1984.

    Article  Google Scholar 

  • Palumbo, M.R, and R. Khaleel, Kriged Estimates of Transmissivity in the Mesilla Bolson, New Mexico, Water Resources Bulletin, 19(6), 929–936, 1983.

    Article  Google Scholar 

  • Parzen, E., On the estimation of a probability density function and mode, Annals ofMathematical Statistics, 33, 1065–1076, 1962.

    Article  Google Scholar 

  • Poggio, T., and F. Girosi, Regularization algorithms for learning that are equivalent to Multilayer Networks, Science, Vol. 247, 978–982, 1990.

    Article  Google Scholar 

  • Ranjitnhan, S. and J. W. Eheart, Neural Network — Based Screening for Groundwater Reclamation Under Uncertainty, Water Resour. Res., 29(3), 563–574, 1993.

    Article  Google Scholar 

  • Rao, B. L. S. P., Nonparametric Functional Estimation, Academic Press, 1983.

    Google Scholar 

  • Rashid, A., Aziz A., and K. F. Wong, A Neural Network Approach to the Determination of Aquifer Parameters, Ground Water, 30(2), 164–166, 1992.

    Article  Google Scholar 

  • Rizzo, D. M, and D. E. Dougherty , Characterization of Aquifer Properties using Artificial Neural Networks: Neural Kriging, Water Resour. Res., 30(2), 483–497, 1994.

    Article  Google Scholar 

  • Rogers, L. L. and F. U. Dowla, Optimization of Groundwater Remediation Using Artigficial Neural Networks with Parallel Solute Transport Modeling, Water Resour. Res., 30(2), 4578–481, 1994.

    Article  Google Scholar 

  • Rumelhart, D. E., G. E. Hinton, and R. J. Williams, Learning representations by back-propagation errors, Nature(London), 323, 533–536, 1986.

    Article  Google Scholar 

  • Russo, D. W., S. K. Rogers, M. Kabrisky, M. E. Oxley, and B. W. Suter, Neural Networks for sonar signal processing, IEEE Conference on Neural networks for Ocean Engineering, Tutorial No. 8, Washington, DC, 1991.

    Google Scholar 

  • Saha, A., and J. D. Keeler, In Algorithms for Better Representation and Faster Learning in Radial Basis Function Networks. Advance in Neural Information Processing Systems 2, Touretzky, D. S. et al., eds., 482–489, San Monteo, CA: Morgan Kaufmann, 1990.

    Google Scholar 

  • Seo, D., W. F. Krajewski, and D. S. Bowles, Stochastic Interpolation of Rainfall Data from Rain Gauges and Radar Using Cokriging:1 . Design of Experiments, Water Resour. Res., 26(3), 468–477, 1990.

    Google Scholar 

  • Shamsi, U.M., and R. G. Quimpo, An Application of Kriging to Rainfall Network Design, Nordic Hydrology, 19, 137–152, 1988.

    Google Scholar 

  • Specht, D. F., Probabilistic Neural Networks, Neural Networks, Vol. 3, 109–118, 1990.

    Article  Google Scholar 

  • Specht, D. F., A General Regression Neural Network, IEEE Trans. on Neural Networks, 2(6), 1991.

    Google Scholar 

  • Spuill, T. B., and L. Candela, Two Approaches to Design of Monitoring Networks, Ground Water, 28(3), 430–442, 1990.

    Article  Google Scholar 

  • Tabios, G. Q., and J. D. Salas, A Comparative Analysis of Techniques for Spatial Interpolation of Precipitation, Water Resources Bulletin, 21(3), 365–380, 1985.

    Article  Google Scholar 

  • Tang, Z., C. Almeida, and P. A. Fishwick, Time series forecasting using neural networks vs. Box-Jenkins methodology, Simulation, 57(5), 303–310, 1991.

    Article  Google Scholar 

  • Venkatram, A., On the Use of Kriging in the Spatial Analysis of Acid Precipitation Data, Atmospheric Environment, 22(9), 1963–1975, 1988.

    Article  Google Scholar 

  • Vogl, T. P., J. K. Mangis, A. K. Rigler, W.K. Zink, and D. L. Alkon, Accelerating the Convergence of Backpropagation method, Biol. Cybern., 59, 257–263, 1988.

    Article  Google Scholar 

  • Web, A. R., and D. Lowe, The Optimal Internal Representation of Multilayer Classifier Networks Performs Nonlinear Discriminant Analysis, Neural Networks, Vol. 3, 367–375, 1990.

    Article  Google Scholar 

  • Werbos, P. J., Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Science, Ph.D. Thesis, Harvard University, Cambridge, MA., 1974.

    Google Scholar 

  • Weigend, A. S., D. E. Runmelhart, and B. A. Huberman, Generalization by Weight-Elimination with Application to Forecasting, in Advances in Neural Information Processing, vol. 3, edited by R.P. Lippman, J. Moody, and D. S. Touretzky, 395–432, Morgan, Kaufman, San Mateo, CA., 1991.

    Google Scholar 

  • White, H., Connectionist Nonparametric Regression: Multi-layer Feed-forward Networks can Learn Arbitrary Mappings, Neural Networks, 3,535–549, 1990.

    Article  Google Scholar 

  • Yates, M. V., and S. R. Yates, Septic tank setback distance: A way to minimize virus contamination of drinking water, Ground Water, 27(2), 1989.

    Google Scholar 

  • Yates, S. R., A. W. Warrick, and D. E. Mayers, Disjunctive Kriging: 1. Overview of Estimation and Conditional Probability, Water Resour. Res., 22(5), 615–621, 1986.

    Article  Google Scholar 

  • Zhang, S. P., H. Watanebe, and R. Yamada, Prediction of Daily Water Demands by Neural Networks, Stochastic and Statistical Methods in Hydrology and Environmental Engineering, Vol. 3, 217–227, Kluwer Academic Publishers, 1994.

    Google Scholar 

  • Zirschky, J., G. P. Keary, R. O. Gilbert, and E. J. Middlebrooks, Spatial estimation of hazardous waste site data, J. of Environmental Engineering, 111(6), 1985.

    Google Scholar 

  • Zhu, M., M. Fujita, and N. Hashimoto, Application of Neural Network to Runoff Prediction, Stochastic and Statistical Methods in Hydrology and Environmental Engineering, Vol. 3, 205–215, Kluwer Academic Publishers, 1994.

    Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Pusan National University, Pusan, South Korea

    Hyun-Suk Shin (Assistant Professor, Professor) & Jose D. Salas

  2. Department of Civil Engineering, Colorado State University, Ft. Collins, CO, 80523, USA

    Hyun-Suk Shin (Assistant Professor, Professor) & Jose D. Salas

Authors
  1. Hyun-Suk Shin
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  2. Jose D. Salas
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Editors and Affiliations

  1. Purdue University, West Lafayette, Indiana, USA

    R. S. Govindaraju  & A. Ramachandra Rao  & 

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Shin, HS., Salas, J.D. (2000). Spatial Analysis of Hydrologic and Environmental Data Based on Artificial Neural Networks. In: Govindaraju, R.S., Rao, A.R. (eds) Artificial Neural Networks in Hydrology. Water Science and Technology Library, vol 36. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9341-0_14

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  • DOI: https://doi.org/10.1007/978-94-015-9341-0_14

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-5421-0

  • Online ISBN: 978-94-015-9341-0

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