An Artificial Neural Network model for mountainous water-resources management: The case of Cyprus mountainous watersheds

Environmental Modelling and Software

Volumn 22, Issue 7, 2007, Pages 1066-1072

  Iliadis, Lazaros S ^a   Maris, Fotis ^a  

^a DEMOCRITUS UNIVERSITY OF THRACE   (Greece)

Author keywords

Artificial neural networks; Back propagation algorithm; Maximum volume of water flow; Water resources management

Indexed keywords

BACKPROPAGATION; FLOW OF WATER; NEURAL NETWORKS; RAIN; WATER RESOURCES; WATERSHEDS;

BACK PROPAGATION ALGORITHM; MOUNTAINOUS WATERSHEDS; WATER RESOURCES MANAGEMENT;

WATER SUPPLY SYSTEMS;

ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; DATA SET; MOUNTAIN REGION; PRECIPITATION INTENSITY; RISK ASSESSMENT; TORRENT; WATER FLOW; WATER MANAGEMENT; WATER SUPPLY; WATERSHED;

ASIA; CYPRUS; EURASIA; MIDDLE EAST;

EID: 33846820421     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2006.05.026     Document Type: Article

References (45)

1. Almasri M.N., and Kaluarachchi J.J. Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. Environmental Modelling and Software. Elsevier Science 20 7 (2005) 851-871
2. Avramidis S., and Iliadis L. Wood-Water Isotherm Prediction with Artificial Neural Networks: a Preliminary Study". Holzforschung, 0018-3830 59 3 (2005) 336-341 Berlin, New York, Walter De Gruyter & Co
3. Bi J., Bennet K.P., Embrechts M., Breneman C.M., and Song M. Dimensionality reduction via sparse support vector machines. Journal of Machine Learning Research 3 (2003) 1229-1243
4. Boillereaux L., Cadet C., and Le Bail A. Thermal properties estimation via real time neural network learning. Journal of Food Engineering 57 (2003) 17-23
5. Bowers, J.A., Shedrow, C.B., 2000. Predicting Stream Water Quality using Artificial Neural Networks. Westinghouse Savannah River Company, SRS Ecology Environmental Information Document, MS-2000-00112. Savannah River Site, Aiken, SC 29808, US Department of Energy.
6. Callan R. The Essence of Neural Networks (1999), Prentice Hall, UK
7. Chang M. Forest Hydrology, An Introduction to Water and Forests (2002), CRC Press
8. Chow Ven T., Maidment R.D., and Lays W.L. Applied Hydrology (1988), McGraw-Hill
9. Chungen Y., Rosendahl L., and Luo Z. Simulation Modelling Practice and Theory. Elsevier Science vol. 11 (2003) 211-222
10. Dibike, Y., Solomatine, D., 1999. River Flow Forecasting Using Artificial Neural Networks European Geophysical Society (EGS) XXIV General Assembly, The Hague, The Netherlands.
11. Epcor company home page (2005).
12. Gaupe D. Principles of Artificial Neural Networks (1997), World Scientific, Singapore
13. Gribbin E.J. Hydraulics and Hydrology for Stormwater Management (1996), Delmar Publishers
14. Guyon I., and Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research 3 (2003) 1157-1182
15. Hamed M.M., Khalafallah M.G., and Hassanien E.A. Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling and Software. Elsevier Science 19 10 (2004) 919-928
16. Haykin S. Neural Networks: A Comprehensive Foundation (1999), Mcmillan College Publishing Company, New York
17. Hussain M.A., Safiur Rahman M., and Ng C.W. Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network. Journal of Food Engineering 51 (2002) 239-248
18. Iliadis, L., Maris, F., 2005. An Artificial Neural Network to Estimate Average Maximum Instant Water-Flow of Watersheds. Proceedings of the ninth International Conference of Engineering Applications of Neural Networks, Lille, France, pp. 215-222.
19. Iliadis, L., Spartalis, S., Maris, F., Marinos, D., 2004. A Decision Support System Unifying Trapezoidal Function Membership Values Using T-Norms: The Case of River Evros Torrential Risk Estimation. Proceedings of the ICNAAM (International Conference in Numerical Analysis and Applied Mathematics) Greece. J. Wiley-VCH Verlag GmbH Publishing Co., Weinheim Germany, pp.173-177, ISBN 3-527-40563-1.
20. Jambunathan K., Hartle S.L., Ashforth-Frost S., and Fontana V.N. Evaluating convective heat transfer coefficients using neural networks. International Journal of Heat Mass Transfer 39 11 (1996) 2329-2332
21. Jacobs R.A. Increased rates of convergence through learning rate adaptation. Neural Networks 1 (1988) 295-307
22. Klohn W. Re-evaluation of Water Resources and of Water Demands of the Island (2002), Department of water development, Ministry of Agriculture, Natural Resources and Environment of the Republic of Cyprus, Organization for the Nutrition and Health of the United Nations
23. Kyprhs D., and Neofytou P. Monthly River Supplies of Cyprus, Monthly Rain-Falls, Maximums of instant flow. Ministry of Agriculture Natural Resources and Environment (1998), Department of Water Development, Cyprus
24. Leondes C. Fyzzy logic and Expert Systems Applications (1998), Academic Press, California, USA
25. Lobbrecht A.H., and Solomatine D.P. Control of Water Levels in Polder Areas Using Neural Networks and Fuzzy Adaptive Systems. Water Industry Systems: Modelling and Optimization Applications vol. 1 (1999), Research Studies Press, Baldock, UK pp. 509-518
26. Maier H.R., and Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling and Software 15 1 (2000) 101-124
27. Maier H.R., Morgan N., and Chow C.W.K. Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environmental Modelling & Software 19 5 (2004) 485-494
28. Mays W.L. Stormwater Collection Systems Design Handbook (2001), McGraw-Hill
29. McCuen R. Hydrologic Analysis and Design. third ed. (2005), Pearson Prentice Hall, Upper Saddle River, New Jersey pp. 108-168
30. Minai, A.A., Williams, R.D., 1990. Acceleration of back-propagation through learning rate and momentum adaptation. International Joint Conference on Neural Networks I, 676-679.
31. Myhara R.M., and Sablani S. Unification of food water sorption isotherms using artificial neural networks. Drying Technology 19 8 (2001) 1543-1554
32. Neuralware, 2001. Getting Started. A Tutorial for Neuralworks Professional II/PLUS. Carnegie, PA, USA.
33. Neuralworks II/Plus Supplement, 2001, Carnegie, PA, USA.
34. Neuralworks Reference Guide, 2001, Carnegie, PA, USA.
35. Paliwal J., Visen N.S., and Jayas D.S. Evaluation of neural network architectures for cereal grain classification using morphological features. Journal of Agricultural Engineering 79 4 (2001) 361-370
36. Picton P. Neural Networks. second ed. (2000), Palgrave, New York, USA
37. Rummelhart D.E., Hinton G.E., and Wiliams R.J. Learning Internal Representations by Error Propagation. Institute for Cognitive Science Report 8506 (1985), University of California, San Diego
38. Rummelhart D.E., Hinton G.E., and Wiliams R.J. Learning Internal Representations by Error Propagation. Parallel Distributed Processing, Explorations in the Microstructure of Cognition vol. 1 (1986), MIT Press, Cambridge, MA Foundations
39. Sablani S.S., Baik O.-D., and Marcotte M. Neural networks for predicting thermal conductivity of bakery products. Journal of Food Engineering 52 (2002) 299-304
40. Sivakumar B. Hydrologic modeling and forecasting: role of thresholds. Environmental Modelling and Software Journal. Elsevier Science 20 5 (2005) 515-519
41. Sreekanth S., Ramaswamy H.S., and Sablani S.S. Prediction of psychrometric parameters using neural networks. Drying Technology 16 3-5 (1998) 825-837
42. Thamarai, S., Malmathanraj, R., 2005. Missile Defense and Interceptor Allocation by Modified Bionet Neural Network. In: Proceedings of the ninth International Conference of Engineering Applications of Neural Networks, Lille, France, pp. 299-306.
43. UNESCO home page, 1998-2000. .
44. Van Looy, S., Meeus, J., Wyns, B., Vander Cruyssen, B., Boullart, L., Keyser, F., 2005. Comparison of Machine Learning Models for Prediction of Dose Increase in Patients with Rheumatoid Arthritis. Proceedings of the ninth International Conference of Engineering Applications of Neural Networks, Lille, France, pp. 189-196.
45. Viessman W., and Lewis L.G. Introduction to Hydrology. fifth ed. (2002), Prentice Hall