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14 Goloka Behari Sahoo and Chittaranjan Ray Maier, H. R. and G.C. Dandy (1996). The use of artificial neural networks for the prediction of water quality parameters, Water Resources Research, 32, 1013-1022. Maier, H. R., G. C. Dandy (1999). Empirical comparison of various methods for training feed-forward neural networks for salinity forecasting, Water Resources Research, 35 (8), 2591-2596. Mishra, A., C. Ray and D.W. Kolpin (2004). Use of qualitative and quantitative information in neural networks for assessing agricultural chemical contamination of domestic wells, J. Hydrol. Eng., 9(6), 502–511. Mujumdar, P.P. and K. Sasikumar (2002). A fuzzy risk approach for seasonal water quality management of a river system. Water Resources Research, 38 (1), (5) 1-(5) 9. Ray, C. and K.K. Klindworth (2000). Neural networks for agrichemical vulnerability assessment of rural private wells, J. Hydrol. Eng., 5(2), 162–171. Sahoo, G. B. and C. Ray (2006). Flow forecasting for a Hawaii stream using rating curves and neural networks, J. Hydrol., 317, 63–80. Sahoo, G.B., C. Ray and H.F. Wade (2005). Pesticide prediction in ground water in North Carolina domestic wells using artificial neural network, J. Ecol. Modell., 183, 29–46. Sahoo, G.B., C. Ray, E. Mehnert and D.A. Keefer (2006). Application of artificial neural networks to assess pesticide contamination in shallow groundwater, Sci. Total Environ., 367, 234–251. Sahoo, G.B. and C. Ray (<strong>2008</strong>). Micro-genetic algorithms and artificial neural networks to assess minimum data requirements for prediction of pesticide concentrations in shallow groundwater on a regional scale. Water Resources Research, doi:10.1029/ 2007WR005875. Schleiter, I.M., D. Borchardt, R. Wagner, T. Dapper, K. Schmidt, H. Schmidt, and H. Werner, (1999). Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks. Ecological Modelling, 120, 271-286. Shi, D., D.S. Yeung and J.Gao (2005). Sensitivity analysis applied to the construction of radial basis function networks, Neural Networks, 18(7), 951–957. Simunek, J., M. Sejna and R. van Genuchten (1998). HYDRUS-1D manual of versions 2.0, U.S. Salinity Laboratory, Riverside, CA. Solley, W.B., R.R. Pierce and H. A. Perlman, (1998). Estimated use of water in the United States in 1995, Circular, 1200, 71 pp., U.S. Geological Survey. The MathWorks, Inc. (2005). MATLAB version 7.1, 3 Apple Hill Drive, Natick, Massachusetts, USA. USDA–ARS (1992). Root zone water quality model (RZQM) v. 1.0., Technical Documentation, GSPR Report 2, USDA–ARS Great Plains Systems Research Unit, Fort Collins, CO. USEPA (1990). National survey of pesticides in drinking water wells, phase I report, EPA 570/9–90–015, U. S. Environmental Protection Agency, Office of Water and Office of Pesticides and Toxic Substances, Washington, D.C. Weber, J.B., R.A. McLaughlin, H.F. Wade, E. Morey, and N.C. Raleigh (1997). Finding and predicting pesticides in ground water in North Carolina, Consumer Environmental Issues: Safety, Health, Chemicals and Textiles in the Near Environment, in International Symposium Proceedings, pp. 211–220, St. Petersburg, FL. Zhang, Y., J. Pulliainen, S. Koponen, and M. Hallikainen, (2002). Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data. Remote Sensing of Environment, 81, 327-336.
RESEARCH AND REVIEW STUDIES
Page 3: GROUNDWATER: MODELLING, MANAGEMENT
Page 6 and 7: Copyright © 2009 by Nova Science P
Page 8 and 9: vi Chapter 8 Chapter 9 Chapter 10 C
Page 10 and 11: viii Luka F. König and Jonas L. We
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Page 19: SHORT COMMUNICATION
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GIS-Based Aquifer Modeling and Plan
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80 Sabrina Saponaro, Sara Puricelli
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100 Sabrina Saponaro, Sara Puricell
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In: Groundwater: Modelling, Managem
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Groundwater Interactions with Surfa
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134 Marek Šváb and Lenka Wimmerov
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Fundamentals of Groundwater Modelli
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6.0. Model Calibration Fundamentals
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2. Subsurface Contaminants Contamin
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Contaminants in Groundwater and the
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6.2. In Groundwater Contaminants in
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Preliminary Studies for Designing a
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Simulation Study Preliminary Studie
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Water Budget Preliminary Studies fo
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Figure 1. Location of the study are
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Table 1. Continued EVENT BEGINNING
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Groundwater Management in the North
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232 Franco Cucchi, Giuliana Frances
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Table 1. Chemical and isotopical co
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Table 1. Continued Borehole ID Type
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Table 2. Univariate overview of the
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Analytical and Numerical Solutions.
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292 K.L. Katsifarakis 1. Introducti
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294 K.L. Katsifarakis fields with v
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296 K.L. Katsifarakis solution to t
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298 K.L. Katsifarakis The aforement
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300 K.L. Katsifarakis If applicatio
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302 K.L. Katsifarakis s w (50m) 96,
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304 K.L. Katsifarakis well coordina
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306 K.L. Katsifarakis injected in a
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308 K.L. Katsifarakis McKinney, D.C
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310 Nigel J. Cassidy 1.0. Introduct
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312 Nigel J. Cassidy separation). U
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314 Nigel J. Cassidy needed to char
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316 Nigel J. Cassidy resolution min
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318 Nigel J. Cassidy al., 2005; Eve
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320 Nigel J. Cassidy heat. As such,
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322 Nigel J. Cassidy substantially
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324 Nigel J. Cassidy 4.2. Conductiv
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326 Nigel J. Cassidy Figure 6. Prin
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328 Nigel J. Cassidy window is then
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330 Nigel J. Cassidy θ - volumetri
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332 Nigel J. Cassidy (2000). In gen
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334 Nigel J. Cassidy The influence
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336 Nigel J. Cassidy Figure 11. Sum
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338 Nigel J. Cassidy 2. An upper ae
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340 Nigel J. Cassidy upper sand lay
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342 Nigel J. Cassidy Bradford J. H.
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344 Nigel J. Cassidy Doolittle, J.
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346 Nigel J. Cassidy Jardani, A., D
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348 Nigel J. Cassidy Revil, A., Nau
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350 Nigel J. Cassidy Weiller, K. W.
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352 Nick Cartwright, Peter Nielsen,
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362 A. Akber, A. Mukhopadhyay, E. A
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392 Index Bangladesh, 188 banks, 11
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394 Index definition, 18, 20, 26, 1
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396 Index goals, 306 government, 19
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398 Index Miami, 349 migration, x,
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400 Index probability, 166, 295 pro
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402 Index specific heat, 90, 108 sp
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404 Index wetting, 86, 349 wind, 21
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