A hybrid neural-genetic algorithm for reservoir water quality management

Water Research

Volumn 40, Issue 7, 2006, Pages 1367-1376

  Kuo, Jan Tai ^a   Wang, Ying Yi ^a   Lung, Wu Seng ^b  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b UNIVERSITY OF VIRGINIA   (United States)

Author keywords

Artificial neural networks; Feitsui Reservoir; Genetic algorithms; Nutrient model; Total phosphorus; Water quality management

Indexed keywords

COMPUTER SIMULATION; CONCENTRATION (PROCESS); MATHEMATICAL MODELS; NEURAL NETWORKS; PHOSPHORUS; RESERVOIRS (WATER); WATER QUALITY;

FEITSUI RESERVOIR; NUTRIENT MODEL; TOTAL PHOSPHORUS; WATER QUALITY MANAGEMENT;

GENETIC ALGORITHMS;

PHOSPHORUS;

COMPUTER SIMULATION; CONCENTRATION (PROCESS); GENETIC ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS; PHOSPHORUS; RESERVOIRS (WATER); WATER QUALITY;

GENETIC ALGORITHM; RESERVOIR; WATER QUALITY;

ARTICLE; ARTIFICIAL NEURAL NETWORK; GENETIC ALGORITHM; MATHEMATICAL COMPUTING; MODEL; NUTRIENT; PRIORITY JOURNAL; PROBLEM SOLVING; RESERVOIR; TAIWAN; WATER MANAGEMENT; WATER QUALITY; WATER STANDARD; WATERSHED MANAGEMENT;

EID: 33645130069     PISSN: 00431354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.watres.2006.01.046     Document Type: Article

References (20)

1. P.A. Aguilera, A.G. Frenich, J.A. Torres, H. Castro, J.L.M. Vidal, and M. Canton Application of the Kohonen neural network in coastal water management: methodological development for the assessment and prediction of water quality Water Res. 35 17 2001 4053 4062
2. S.C. Chapra Surface Water-Quality Modeling 1997 McGraw-Hill New York
3. D.J. Choi, and H. Park A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process Water Res. 35 16 2001 3959 3967
4. D.A. Coley An Introduction to Genetic Algorithms for Scientists and Engineers 1999 World Scientific Singapore
5. J.A. Freeman, and D.M. Skapura Neural Networks: Algorithms, Applications, and Programming Techniques 1991 Addison-Wesley Reading, MA
6. W.R. Huang, and S. Foo Neural network modeling of salinity variation in Apalachicola River Water Res. 36 1 2002 356 362
7. J.S.R. Jang, C.T. Sun, and E. Mizutani Neuro-Fuzzy and Soft Computing 1997 Prentice-Hall Englewood Cliffs, NJ
8. C. Karul, S. Soyupak, A.F. Cilesiz, N. Akbay, and E. Germen Case-studies on the use of neural networks in eutrophication modeling Ecol. Model. 134 2000 145 152
9. J.T. Kuo, and J.H. Wu A nutrient model for a lake with time-variable volumes Water Sci. Technol. 24 6 1991 133 139
10. D.R. Maidment Handbook of Hydrology 1992 McGraw-Hill New York
11. H.R. Maier, and G.C. Dandy Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications Environ. Model. Software 15 1 2000 101 124
12. G.K. Nurnberg Hypolimnetic withdrawal as lake restoration technique J. Environ. Eng.-ASCE 113 5 1987 1006 1017
13. P. Srivastava, J.M. Hamlett, P.D. Robillard, and R.L. Day Watershed optimization of best management practices using AnnAGNPS and a genetic algorithm Water Resour. Res. 38 3 2002
14. R.V. Thomann, and J.A. Muller Principles of Surface Water Quality Modeling and Control 1987 Harper and Row New York
15. C.P. Tung, S.Y. Hsu, C.M. Liu, and J.S. Li Application of the genetic algorithm for optimizing operation rules of the LiYuTan Reservoir in Taiwan J. Am. Water Resour. Assoc. 39 3 2003 649 657
16. T.L. Veith, M.L. Wolfe, and C.D. Heatwole Optimization procedure for cost effective BMP placement at a watershed scale J. Am. Water Resour. Assoc. 39 6 2003 1331 1343
17. R.A. Vollenweider Input-output models with special reference to the phosphorous loading concept in limnology Schweiz. Z. Hydrol. 37 1975 53 83
18. L.F.A. Wessels, and E. Barnard Avoiding false local minima by proper initialization of connections IEEE Trans. Neural Netw. 3 6 1992 899 905
19. H. Wilson, and F. Recknagel Towards a generic artificial neural network model for dynamic predictions of algal abundance in freshwater lakes Ecol. Model. 146 1-3 2001 69 84
20. R. Zou, W.S. Lung, and H.C. Guo Neural network embedded Monte Carlo approach for water quality modeling under input information uncertainty J. Comput. Civ. Eng. 16 2 2002 135 142