Machine learning for predictive management: Short and long term prediction of phytoplankton biomass using genetic algorithm based recurrent neural networks

International Journal of Environmental Research

Volumn 6, Issue 1, 2012, Pages 95-108

  Kim, D K ^a   Jeong, K S ^b   McKay, R I B ^a   Chon, T S ^b   Joo, G J ^a  

^a Seoul National University   (South Korea)

^b Pusan National University   (South Korea)

Author keywords

Biomass; Genetic algorithm; Management; Nakdong river; Sensitivity analysis; Time series prediction

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BIOLOGICAL ANALYSIS; BIOMASS; CHLOROPHYLL A; DIATOM; ECOLOGICAL MODELING; GENETIC ALGORITHM; PHYSICOCHEMICAL PROPERTY; PHYTOPLANKTON; PREDICTION; RIVER WATER; SENSITIVITY ANALYSIS; SMOOTHING; TIME SERIES ANALYSIS;

NAKDONG RIVER; SOUTH KOREA;

ALGAE; BACILLARIOPHYTA; CYANOBACTERIA; MICROCYSTIS AERUGINOSA; STEPHANODISCUS HANTZSCHII;

EID: 84862965642     PISSN: 17356865     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (72)

1. Blanco, A., Delgado, M. and Pegalajar, M. C. (2000). A genetic algorithm to obtain the optimal recurrent neural network. International Journal of Approximate Reasoning, 23, 67-83.
2. Chon, T. S., Park, Y. S. and Cha, E. Y. (2000). Patterning of community changes in bentic macroinvertebrates collected from urbanized streams for the short term prediction by temporal artificial neuronal networks. (In S. Lek and J.F. Guegan (Eds.), Artificial Neuronal Networks: Application to ecology and evolution. (pp. 99-114). Berlin: Springer.)
3. Chon, T. S., Park, Y. S., Moon, K. H. and Cha, E. Y. (1996). Patterning communities by using an artificial neural network. Ecological Modelling, 90, 69-78.
4. Co, H. C. and Boosarawongse, R. (2007). Forecasting Thailand's rice export: Statistical techniques vs. artificial neural networks. Computers and Industrial Engineering, 53 (4), 610-627.
5. Curry, B. (2007). Neural networks and seasonality: Some technical considerations. European Journal of Operational Research, 179 (1), 267-274.
6. Einsle, U. (1993). Crustacea, Copepoda, Calanoida und Cyclopoida. Süßwasserfauna von Mitteleuropa. (Stuttgart: Gustav Fisher Verlag).
7. Fielding, A. (1999). An introduction to machine learning methods. (In A. Fielding (Ed.), Machine Learning Methods for Ecological Applications. (pp. 1-35). Massachusetts: Kluwer Academic Publishers.)
8. Franses, P. H. and Draisma, G. (1997). Recognizing changing seasonal patterns using artificial neural networks. Journal of Econometrics, 81 (1), 273-280.
9. Gençay, R. (1996). Non-linear prediction of security returns with moving average rules. Journal of Forecasting, 15 (3), 165-174.
10. Goethals, P. L. M., Dedecker, A. P., Gabriels, W., Lek, S. and Pauw, N. D. (2007). Applications of artificial neural networks predicting macroinvertebrates in freshwaters. Aquatic Ecology, 41 (3), 491-508.
11. Gosselain, V., Descy, J.-P., Viroux, L., Joaquim-Justo, C., Hammer, A., Métens, A. and Schweitzer, S. (1998). Grazing by large river zooplankton: a key to summer potamoplankton decline? The case of the Meuse and Moselle rivers in 1994 and 1995. Hydrobiologia, 369/370, 199-216.
12. Ha, K., Cho, E.-A., Kim, H.-W. and Joo, G.-J. (1999). Microcystis bloom formation in the lower Nakdong River, South Korea: importance of hydrodynamics and nutrient loading. Marine and Freshwater Research, 50, 89-94.
13. Ha, K., Jang, M.-H. and Joo, G.-J. (2003). Winter Stephanodiscus bloom development in the Nakdong River regulated by an estuary dam and tributaries. Hydrobiologia, 506/509, 221-227.
14. Ha, K. and Joo, G.-J. (2000). Role of silica in phytoplankton succession: an enclosure experiment in the downstream Nakdong River (Mulgum), Korean Journal of Ecology, 23 (4), 299-307.
15. Ha, K., Kim, H.-W., Jeong, K.-S. and Joo, G.-J. (2000). Vertical distribution of Microcystis population in the regulated Nakdong River, Korea, Limnology, 1, 225-230.
16. Ha, K., Kim, H. W. and Joo, G. J. (1998). The phytoplankton succession in the lower part of hypertrophic Nakdong River (Mulgum), South Korea. Hydrobiologia, 370, 217-227.
17. Harding, L. W. and Perry, E. S. (1997). Long-term increase of phytoplankton biomass in Chesapeake Bay, 1950-1994. Marine Ecology Progress Series, 157, 39-52.
18. Hibon, M. and Evgeniou, T. A. (2005). Simple procedure for reliability of repairable systems. International Journal of Forecasting, 21, 15-24.
19. Ho, S. L., Xie, M. and Goh, T. N. (2002). A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction. Computers and Industrial Engineering, 42 (2-4), 371-375.
20. Huang, W. and Foo, S. (2002). Neural network modeling of salinity variation in Apalachicola River. Water Research, 36, 356-362.
21. Jørgensen, S. E. (1992). Integration of Ecosystem Theories: A Pattern. (Dordrecht: Kluwer)
22. Jeong, K.-S., Joo, G.-J., Kim, H.-W., Ha, K. and Recknagel, F. (2001). Prediction and elucidation of phytoplankton dynamics in the Nakdong River (Korea) by means of a recurrent artificial neural network. Ecological Modelling, 146, 115-129.
23. Jeong, K.-S., Kim, D.-K., Chon, T.-S. and Joo, G.-J. (2005). Machine learning application to the Korean freshwater ecosystems. Korean Journal of Ecology, 28 (6), 405-415.
24. Jeong, K.-S., Kim, D.-K. and Joo, G.-J. (2006a). River phytoplankton prediction model by Artificial Neural Network: Model performance and selection of input variables to predict time-series phytoplankton proliferations in a regulated river system. Ecological Informatics, 1 (3), 235-245.
25. Jeong, K.-S., Kim, D.-K. and Joo, G.-J. (2007). Delayed influence of dam storage and discharge on the determination of seasonal proliferations of Microcystis aeruginosa and Stephanodiscus hantzschii in a regulated river system of the lower Nakdong River (South Korea). Water Research, 41 (6), 1269-1279.
26. Jeong, K.-S., Kim, D.-K., Jung, J.-M., Kim, M.-C. and Joo, G.-J. (2008). Non-linear autoregressive modelling by Temporal Recurrent Neural Networks for the prediction of freshwater phytoplankton dynamics. Ecological Modelling, 211(3-4), 292-300.
27. Jeong, K.-S., Kim, D.-K., Whigham, P. and Joo, G.-J. (2003). Modelling Microcystis aeruginosa bloom dynamics in the Nakdong River by means of evolutionary computation and statistical approach, Ecological Modelling, 161, 67-78.
28. Jeong, K.-S., Recknagel, F. and Joo, G.-J. (2006b). Prediction and Elucidation of Population Dynamics of a Blue-green Algae (Microcystis aeruginosa) and Diatom (Stephanodiscus hantzschii) in the Nakdong River-Reservoir System (South Korea) by Artificial Neural Networks. (In F. Recknagel (Ed.), Ecological Informatics: Scope, Techniques and Applications. (pp. 255-273). Berlin: Springer.)
29. Joo, G.-J. and Jeong, K.-S. (2005). Modelling community changes of cyanobacteria in a flow regulated river (the lower Nakdong River, S. Korea) by means of a Self-Organizing Map (SOM). (In S. Lek and M. Scardi and P.F.M. Verdonschot and J.-P. Descy and Y.-S. Park (Eds.), Modelling Community Structure in Freshwater Ecosystems. (pp. 273-287). Berlin: Springer.)
30. Joo, G.-J., Kim, H.-W., Ha, K. and Kim, J.-K. (1997). Long-term trend of the eutrophication of the lower Nakdong River, Korean Journal of Limnology, 30 (supplement), 472-480.
31. Joy, M. K. and Death, R. G. (2004). Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neual networks, Freshwater Biology, 49, 1036-1052.
32. Kim, D.-K., Jeong, K.-S., Whigham, P. A. and Joo, G.-J. (2007). Winter diatom blooms in a regulated river in South Korea: explanations based on evolutionary computation. Freshwater Biology, 52 (10), 2021-2041.
33. Kim, H.-W., Hwang, S.-J. and Joo, G.-J. (2000). Zooplankton grazing on bacteria and phytoplankton in a regulated large river (Nakdong River, Korea). Journal of Plankton Research, 22, 1559-1577.
34. Kimoto, T., Asakawa, K., Yoda, M. and Takeoka, M. (1990). Stock market prediction system with modular neural networks. (Paper presneted at International Joint Conference on Neural Networks, IEEE, San Diego)
35. Koste, W. (1978). Rotatoria. Die Raderiere Mitteleuropas. Ein Bestimmungswerk begrundet von Max Voift. (Stuttgart: Borntrager)
36. Lampert, W. and Sommer, U. (2007). Limnoecology. (New York: Oxford University Press Inc.)
37. Large, A. R. and Petts, G. E. (1992). Rehabilitation of river margins. (Oxford: Blackwell Scientific Publication).
38. Lee, S., Choi, J.-M. and Jeong, K.-S. (2009). Water quality simulation at Mulgeum station (Nakdong River) using zooplankton community data. Journal of Korean Society of Water Quality, 25 (6), 832-839.
39. Lefebvre, C., Fancourt, C., Principe, J., Gerstenberger, J., Samson, D., Euliano, N., Wooten, D., Lynn, G., Geniesse, G., Allen, M., Lucas, M. and Marossero, D. (2005). NeuroSolution 5.0. NeuroDimension, Inc.
40. Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J. and Aulagnier, S. (1996). Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling, 90 (1), 39-52.
41. Liang, Y. H. (2009). Combining seasonal time series ARIMA method and neural networks with genetic algorithms for predicting the production value of the mechanical industry in Taiwan. Neural Computing and Applications, 18 (7), 833-841.
42. Lillo, M. P. Y., Perez-Correa, R., Latrille, E., Fernandez, M., Acuna, G. and Agosin, E. (2000). Data processing for solid substrate cultivation bioreactors. Bioprocess Engineering, 22 (4), 291-297.
43. Maier, H. R. and Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling and Software, 15, 101-124.
44. Maier, H. R., Sayed, T. and Lence, B. J. (2001). Forecasting cyanobacterium Anabaena spp. in the River Murray, South Australia, using B-spline neurofuzzy models. Ecological Modelling, 146, 85-96.
45. Millie, D. F., Weckman, G. R., Paerl, H. W., Pinckney, J. L., Bendis, B. J., Pigg, R. J. and Fahnenstiel, G. L. (2006). Neural net modeling of estuarine indicators: Hindcasting phytoplankton biomass and net ecosystem production in the Neuse (North Carolina) and Trout (Florida) Rivers, USA. Ecological Indicators, 6, 589-608.
46. Mitrovic, S. M., Chessman, B. C., Bowling, L. C. and Richard H. Cooke (2006). Modelling suppression of cyanobacterial blooms by flow management in a lowland river. River Research and Applications, 22 (1), 109-114.
47. Moss, B. (1998). Ecology of Fresh Waters: Man and Medium, Past to Future. (Osney Mead: Blackwell Science Ltd.)
48. Murakami, T., Kuroda, N. and Tanaka, T. (1998). Effects of a rivermouth barrage on planktonic algal development in the lower Nagara river, central Japan, Japanese Journal of Limnology, 59 (3), 251-262.
49. Nelson, M., Hill, T., Remus, W. and O'connor, M. (1999). Time series forecasting using neural networks: Should the data be deseasonalized first?, Journal of Forecasting, 18 (5), 359-367.
50. Oh, H.-M., Ahn, C.-Y., Lee, J.-W., Chon, T.-S., Choi, K. H. and Park, Y.-S. (2007). Community patterning and identification of predominant factors in algal bloom in Daechung Reservoir (Korea) using artificial neural networks. Ecological Modelling, 203 (1-2), 109-118.
51. Papale, D. and Valentini, R. (2003). A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization. Global Change Biology, 9 (4), 525-535.
52. Principe, J. C., Euliano, N. R. and Lefebvre, W. C. (2000). Neural and adaptive systems: Fundamentals through simulations. (New York: John Wiley and Sons, Inc.)
53. Recknagel, F., Cao, H., Kim, B., Takamura, N. and Welk, A. (2006a). Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation. Ecological Informatics, 1 (2), 133-151.
54. Recknagel, F., Talib, A. and Van Der Molen, D. (2006b). Phytoplankton community dynamics of two adjacent Dutch lakes in response to seasons and eutrophication control unravelled by non-supervised artificial neural networks. Ecological Informatics, 1 (3), 277-285.
55. Reynolds, C. S. (1992). Algae. (In P. Calow and G.E. Petts (Eds.), The River Handbook: Hydrological and Ecological Principles. (pp. 195-215). Oxford: Blackwell Scientific Publication.)
56. Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986). Learning representations by backpropagation errors. Nature, 323, 533-536.
57. Schumann, G. and Lauener, G. (2005). Application of a degree-day snow depth model to a Swiss glacierised catchment to improve neural network discharge forecasts. Nordic Hydrology, 36 (2), 99-111.
58. Sharp, J. J. and Howe, L. J. (2000). The Sarawak River barrage - hydrotechnical and geotechnical aspects, Proceeding of the Institution of Civil Engineers. Water, Maritme and Energy, 142 (2), 87-96.
59. Smirnov, N. N. and Timms, B. V. (1983). A revision of the Australian Cladocera (Crustacea). Records of the Australian Museum Supplement, 1, 1-132.
60. Song, H. and Li, G. (2008). Tourism demand modelling and forecasting - A review of Recent research. Tourism Management, 29 (2), 203-220.
61. Velo-Suárez, L. and Gutiérrez-Estrada, J. C. (2007). Artificial neural network approaches to one-step weekly prediction of Dinophysis acuminata blooms in Huelva (Western Andalucía, Spain), Harmful Algae.
62. Walter, M., Recknagel, F., Carpenter, C. and Bormans, M. (2001). Predicting eutrophication effects in the Burrinjuck Reservoir (Australia) by means of the deterministic model SALMO and the recurrent neural network model ANNA. Ecological Modelling, 146 (1-3), 97-113.
63. Wang, H., Wang, J. P. and Tian, W. F. (2006). A seasonal GRBF network for nonstationary time series prediction. Measurement Science and Technology, 17 (10), 2806-2810.
64. Wetzel, R. G. (1983). Limnology. (Philadelphia: Saunders College Publishing)
65. Wetzel, R. G. and Likens, G. E. (1991). Limnological Analysis. (New York: Springer-Verlag)
66. Whigham, P. A. and Recknagel, F. (2001). Predicting chlorophyll-a in freshwater lakes by hybridising process based models and genetic algorithms. Ecological Modelling,146, 243-251.
67. Wu, C. L., Chau, K. W. and Li, Y. S. (2009). Methods to improve neural network performance in daily flows prediction. Journal of Hydrology,372 (1-4), 80-93.
68. Yao, X. (1999). Evolving Artificial Neural Networks. Proceedings of the IEEE, 87, 1423-1447.
69. Yao, X. and Liu, Y. (2001). Evolving neural networks for chlorophyll-a prediction. (Paper presneted at Fourth International Conference on Computational Intelligence and Multimedia Applications Yokusika City, Japan).
70. Yoo, H.-S. (2002). Statistical analysis of factors affecting the Han River water quality. Journal of Korean Society of Environmental Engineers, 24 (12), 2139-2150.
71. Zhang, G. P. and Kline, D. M. (2007). Quarterly time-series forecasting with neural networks. IEEE Transactions on Neural Networks, 18 (6), 1800-1814.
72. Zhang, G. P. and Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160 (2), 501-514.