바로가기메뉴

본문 바로가기 주메뉴 바로가기

ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

logo

  • ENGLISH
  • P-ISSN2287-8327
  • E-ISSN2288-1220
  • SCOPUS, KCI

Model development in freshwater ecology with a case study using evolutionary computation

Journal of Ecology and Environment / Journal of Ecology and Environment, (P)2287-8327; (E)2288-1220
2010, v.33 no.4, pp.275-288
김동균 (서울대학교)
정광석 (부산대학교)
Robert Ian (Bob) McKay (서울대학교)
전태수 (부산대학교)
김현우 (순천대학교)
주기재 (부산대학교)

Abstract

Ecological modeling faces some unique problems in dealing with complex environment-organism relationships,making it one of the toughest domains that might be encountered by a modeler. Newer technologies and ecosystem modeling paradigms have recently been proposed, all as part of a broader effort to reduce the uncertainty in models arising from qualitative and quantitative imperfections in the ecological data. In this paper, evolutionary computation modeling approaches are introduced and proposed as useful modeling tools for ecosystems. The results of our case study support the applicability of an algal predictive model constructed via genetic programming. In conclusion, we propose that evolutionary computation may constitute a powerful tool for the modeling of highly complex objects, such as river ecosystems.

keywords
complex river ecosystem, data learning process, ecological modeling, evolutionary computation, phytoplankton proliferation, time-series prediction

참고문헌

1.

Adams DC, Gurevitch J, Rosenberg MS. 1997. Resampling tests for meta-analysis of ecological data. Ecology 78: 1277-1283.

2.

Ahmed JA, Sarma AK. 2005. Genetic algorithm for optimal operating policy of a multipurpose reservoir. Water Resour Manag 19: 145-161.

3.

An KG, Park SJ, Choi SM, Park JS. 2006. Comparative analysis of long-term water quality data monitored in Andong and Imha Reservoirs. Korean J Limnol 39: 21-31.

4.

Arhonditsis GB, Brett MT. 2005. Eutrophication model for Lake Washington (USA): part I. model description and sensitivity analysis. Ecol Model 187: 140-178.

5.

Atanasova N, Recknagel F, Todorovski L, Dzeroski S, Kompare B. 2006. Computational assemblage of ordinary differential equations for chlorophyll-a using a lake process equation library and measured data of Lake Kasumigaura. In: Ecological Informatics: Scope, Techniques and Applications (Recknagel F, ed). Springer-Verlag, Berlin, pp 409-428.

6.

Bobbin J, Recknagel F. 2001. Knowledge discovery for prediction and explanation of blue-green algal dynamics in lakes by evolutionary algorithms. Ecol Model 146: 253-262.

7.

Boerema LK, Gulland JA. 1973. Stock assessment of the peruvian anchovy (Engraulis ringens) and management of the fishery. J Fish Res Board Can 30: 2226-2235.

8.

Brown LC, Barnwell TO Jr. 1987. The Enhanced Stream Water Quality Models QUAL2E and QUAL2E-UNCAS: Documentation and User Manual. EPA/600/3-87/007. U.S. Environmental Protection Agency, Athens, GA.

9.

Cao H, Recknagel F, Cetin L, Zhang B. 2008. Process-based simulation library SALMO-OO for lake ecosystems: part 2. multi-objective parameter optimization by evolutionary algorithms. Ecol Inform 3: 181-190.

10.

Cao H, Recknagel F, Joo GJ, Kim DK. 2006. Discovery of predictive rule sets for chlorophyll-a dynamics in the Nakdong River (Korea) by means of the hybrid evolutionary algorithm HEA. Ecol Inform 1: 43-53.

11.

Cetin L, Zhang B, Recknagel F. 2005. Process-based simulation library SALMO-OO for lake ecosystems. International Congress on Modelling and Simulation, 2005 Dec 12-15, Melbourne, pp 318-324.

12.

Chapra SC, Reckhow KH. 1983. Engineering Approaches for Lake Management. Vol. II: Mechanistic Modeling. Butterworth Publishers, Boston, MA.

13.

Cho JH, Lee CH. 2009. Parameter optimization of QUAL2K using influence coefficient algorithm and genetic algorithm. J Environ Impact Assess 18: 99-109.

14.

Cho JH, Sung KS. 2004. A study on the river water quality management model using genetic algorithm. J Korean Soc Water Wastewater 18: 453-460.

15.

Cho JH, Sung KS, Ha SR. 2004. A river water quality manage ment model for optimising regional wastewater treatment using a genetic algorithm. J Environ Manag 73: 229-242.

16.

Choi JK, Chung S, Ryoo JI. 2008. Comparative evaluation of QUAL2E and QUAL-NIER models for water quality prediction in eutrophic river. J Korean Soc Water Qual 24: 54-62.

17.

Chon TS, Park YS, Cha EY. 2000. Patterning of community changes in bentic macroinvertebrates collected from urbanized streams for the short term prediction by temporal artificial neuronal networks. In: Artificial Neuronal Networks: Application to Ecology and Evolution (Lek S, Guegan JF, eds). Springer, Berlin.

18.

Chon TS, Kwak IS, Park YS, Kim TH, Kim Y. 2001. Patterning and short-term predictions of benthic macroinvertebrate community dynamics by using a recurrent artificial neural network. Ecol Model 146: 181-193.

19.

Cloern JE. 1996. Phytoplankton bloom dynamics in coastal ecosystems: a review with some general lessons from sustained investigation of San Francisco Bay, California. Rev Geophys 34: 127-168.

20.

Deaton ML, Winebrake JJ. 2000. Dynamic Modeling of Environmental Systems. Springer-Verlag, New York, NY.

21.

Dolk DR. 2000. Integrated model management in the data warehouse era. Eur J Oper Res 122: 199-218.

22.

Dorado J, Rabuñal J, Puertas J, Santos A, Rivero D. 2002. Prediction and modelling of the flow of a typical urban basin through genetic programming. In: Applications of Evolutionary Computing (Cagnoni S, Gottlieb J, Hart E, Middendorf M, Raidl G, eds). Springer, Berlin, pp 190-201.

23.

Everbecq E, Gosselain V, Viroux L, Descy JP. 2001. Potamon: a dynamic model for predicting phytoplankton composition and biomass in lowland rivers. Water Res 35: 901-912.

24.

Fielding AH. 1999. An introduction to machine learning methods. In: Machine Learning Methods for Ecological Applications (Fielding AH, ed). Kluewer Academic Publishers, Norwell, MA.

25.

Giraudel JL, Lek S. 2001. A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecol Model 146: 329-339.

26.

Goethals P, Dedecker A, Gabriels W, De Pauw N. 2003. Development and application of predictive river ecosystem models based on classification trees and artificial neural networks. In: Ecological Informatics (Recknagel F, ed). Springer-Verlag, New York, NY, pp 91-107.

27.

Goethals PLM, Dedecker AP, Gabriels W, Lek S, De Paw N. 2007. Applications of artificial neural networks predicting macroinvertebrates in freshwaters. Aquat Ecol 41: 491-508.

28.

Ha K, Joo GJ. 2000. Role of silica in phytoplankton succession: an enclosure experiment in the downstream Nakdong River (Mulgum). Korean J Ecol 23: 299-307.

29.

Ha K, Jang MH, Joo GJ. 2003. Winter Stephanodiscus bloom development in the Nakdong River regulated by an estuary dam and tributaries. Hydrobiologia 506: 221-227.

30.

Ha K, Cho EA, Kim HW, Joo GJ. 1999. Microcystis bloom formation in the lower Nakdong River, South Korea: importance of hydrodynamics and nutrient loading. Mar Freshw Res 50: 89-94.

31.

Håkanson L, Boulion VV. 2003. A general dynamic model to predict biomass and production of phytoplankton in lakes. Ecol Model 165: 285-301.

32.

Harding LW, Perry ES. 1997. Long-term increase of phytoplankton biomass in Chesapeake Bay, 1950-1994. Mar Ecol Prog Ser 157: 39-52.

33.

Holland JH. 1975. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor, MI.

34.

Icaga Y. 2005. Genetic algorithm usage in water quality monitoring networks optimization in Gediz (Turkey) river basin. Environ Monit Assess 108: 261-277.

35.

Jeong KS, Recknagel F, Joo GJ. 2006. 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: Ecological Informatics: Scope, Techniques and Applications (Recknagel F, ed). Springer, Berlin, pp 255-273.

36.

Jeong KS, Kim DK, Whigham P, Joo GJ. 2003. Modelling Microcystis aeruginosa bloom dynamics in the Nakdong River by means of evolutionary computation and statistical approach. Ecol Model 161: 67-78.

37.

Jeong KS, Joo GJ, Kim HW, Ha K, Recknagel F. 2001. Prediction and elucidation of phytoplankton dynamics in the Nakdong River (Korea) by means of a recurrent artificial neural network. Ecol Model 146: 115-129.

38.

Jeong KS, Kim DK, Jung JM, Kim MC, Joo GJ. 2008. Non-linear autoregressive modelling by Temporal Recurrent Neural Networks for the prediction of freshwater phytoplankton dynamics. Ecol Model 211: 292-300.

39.

Joo GJ, Kim HW, Ha K, Kim JK. 1997. Long-term trend of the eutrophication of the lower Nakdong River. Korean J Limnol 30 Suppl: 472-480.

40.

Jørgensen SE. 1992. Integration of Ecosystem Theories: A Pattern. Kluwer Academic Publishers, Dordrecht.

41.

Khu ST, Liong SY, Babovic V, Madsen H, Muttil N. 2001. Ge netic programming and its application in real-time runoff forecasting. J Am Water Resour Assoc 37: 439-451.

42.

Kilham P, Kilham SS, Hecky RE. 1986. Hypothesized resource relationships among African planktonic diatoms. Limnol Oceanogr 31: 1169-1181.

43.

Kim DK, Jeong KS, Whigham PA, Joo GJ. 2007a. Winter diatom blooms in a regulated river in South Korea: explanations based on evolutionary computation. Freshw Biol 52: 2021-2041.

44.

Kim DK, Cao H, Jeong KS, Recknagel F, Joo GJ. 2007b. Predictive function and rules for population dynamics of Microcystis aeruginosa in the regulated Nakdong River (South Korea), discovered by evolutionary algorithms. Ecol Model 203: 147-156.

45.

Kim G, Kim Y, Song M, Ji K, Yu P, Kim C. 2007c. Evaluation of water quality charateristics in the Nakdong River using multivariate analysis. J Korean Soc Water Qual 23: 814-821.

46.

Kim HW, Ha K, Joo GJ. 1998. Eutrophication of the lower Nakdong River after the construction of an estuarine dam in 1987. Int Rev Hydrobiol 83: 65-72.

47.

Kim HW, Hwang SJ, Joo GJ. 2000. Zooplankton grazing on bacteria and phytoplankton in a regulated large river (Nakdong River, Korea). J Plankton Res 22: 1559-1577.

48.

Kim LH, Choi E, Gil KI, Stenstrom MK. 2004. Phosphorus release rates from sediments and pollutant characteristics in Han River, Seoul, Korea. Sci Total Environ 321: 115-125.

49.

Koza JR. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, New York, NY.

50.

Lavric V, Iancu P, Pleşu V. 2005. Genetic algorithm optimisation of water consumption and wastewater network topology. J Clean Prod 13: 1405-1415.

51.

Lee KS, Chung ES. 2004. Optimal operation rules for multireservoir systems using genetic algorithm. J Korean Soc Civil Eng 24: 9-17.

52.

Lek S. 2007. Uncertainty in ecological models. Ecol Model 207: 1-2.

53.

Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S. 1996. Application of neural networks to modelling nonlinear relationships in ecology. Ecol Model 90: 39-52.

54.

Lotka AJ. 1925. Elements of Physical Biology. Dover Publications, New York, NY.

55.

Magadza CHD. 1980. The distribution of zooplankton in the Sanyati Bay, Lake Kariba: a multivariate analysis. Hydrobiologia 70: 57-67.

56.

Makkeasorn A, Chang NB, Li J. 2009. Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed. J Environ Manag 90: 1069-1080.

57.

Matta JF, Marshall HG. 1984. A multivariate analysis of phytoplankton assemblages in the western North Atlantic. J Plankton Res 6: 663-675.

58.

McKay RIB, Hao HT, Mori N, Hoai NX, Essam D. 2006. Model-building with interpolated temporal data. Ecol Inform 1: 259-268.

59.

McNyset KM. 2005. Use of ecological niche modelling to predict distributions of freshwater fish species in Kansas. Ecol Freshw Fish 14: 243-255.

60.

Mishra AK, Desai VR. 2006. Drought forecasting using feed-forward recursive neural network. Ecol Model 198: 127-138.

61.

Odum HT. 1983. Ecological and General Systems: An Introduction to Systems Ecology. University Press of Colorado, Niwot, CO.

62.

Paik K, Kim JH, Kim HS, Lee DR. 2005. A conceptual rainfall-runoff model considering seasonal variation. Hydrol Process 19: 3837-3850.

63.

Park SS, Lee YS. 2002. A water quality modeling study of the Nakdong River, Korea. Ecol Model 152: 65-75.

64.

Park SY, Choi JH, Wang S, Park SS. 2006a. Design of a water quality monitoring network in a large river system using the genetic algorithm. Ecol Model 199: 289-297.

65.

Park YS, Tison J, Lek S, Giraudel JL, Coste M, Delmas F. 2006b. Application of a self-organizing map to select representative species in multivariate analysis: a case study determining diatom distribution patterns across France. Ecol Inform 1: 247-257.

66.

Pelletier GJ, Chapra SC, Tao H. 2006. QUAL2Kw: a framework for modeling water quality in streams and rivers using a genetic algorithm for calibration. Environ Model Software 21: 419-425.

67.

Peterson AT, Ball LG, Cohoon KP. 2002. Predicting distributions of Mexican birds using ecological niche modelling methods. Ibis 144: E27-E32.

68.

Rabuñal JR, Puertas J, Suárez J, Rivero D. 2007. Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks. Hydrol Process 21: 476-485.

69.

Recknagel F. 2001. Applications of machine learning to ecological modelling. Ecol Model 146: 303-310.

70.

Recknagel F. 2006. Ecological Informatics: Scope, Techniques and Applications. Springer-Verlag, Berlin.

71.

Recknagel F, Benndorf J. 1982. Validation of the ecological simulation model “SALMO”. Int Rev Gesamten Hydrobiol 67: 113-125.

72.

Recknagel F, Bobbin J, Whigham P, Wilson H. 2002. Comparative application of artificial neural networks and genetic algorithms for multivariate time-series modelling of algal blooms in freshwater lakes. J Hydroinformatics 4: 125-133.

73.

Recknagel F, van Ginkel C, Cao H, Cetin L, Zhang B. 2008. Generic limnological models on the touchstone: testing the lake simulation library SALMO-OO and the rule-based Microcystis agent for warm-monomictic hypertrophic lakes in South Africa. Ecol Model 215: 144-158.

74.

Romo S, Van Donk E, Gylstra R, Gulati R. 1996. A multivariate analysis of phytoplankton and food web changes in a shallow biomanipulated lake. Freshw Biol 36: 683-696.

75.

Savic DA, Walters GA, Davidson JW. 1999. A genetic programming approach to rainfall-runoff modelling. Water Resour Manag 13: 219-231.

76.

Schaefer MB. 1968. Methods of estimating effects of fishing on fish populations. Trans Am Fish Soc 97: 231-241.

77.

Shan Y, Paull D, McKay RI. 2006. Machine learning of poorly predictable ecological data. Ecol Model 195: 129-138.

78.

Shim SB, Oh YS, Lee YS, Koh DK. 1995. Eutrophication forecasting of Daechong Reservoir using WASP5 water quality model. J Inst Constr Technol 14: 41-53.

79.

Silvert W. 1997. Ecological impact classification with fuzzy sets. Ecol Model 96: 1-10.

80.

Stockman AK, Beamer DA, Bond JE. 2006. An evaluation of a GARP model as an approach to predicting the spatial distribution of non-vagile invertebrate species. Divers Distrib 12: 81-89.

81.

ter Braak CJF, Verdonschot PFM. 1995. Canonical correspondence analysis and related multivariate methods in aquatic ecology. Aquat Sci 57: 255-289.

82.

Underwood EC, Klinger R, Moore PE. 2004. Predicting patterns of non-native plant invasions in Yosemite National Park, California, USA. Divers Distrib 10: 447-459.

83.

van Tongeren OFR, van Liere L, Gulati RD, Postema G, Boesewinkel-De Bruyn PJ. 1992. Multivariate analysis of the plankton communities in the Loosdrecht lakes: relationship with the chemical and physical environment. Hydrobiologia 233: 105-117.

84.

Volterra V. 1926. Fluctuations in the abundance of a species considered mathematically. Nature 118: 558-560.

85.

Welk A, Recknagel F, Cao H, Chan WS, Talib A. 2008. Rule-based agents for forecasting algal population dynamics in freshwater lakes discovered by hybrid, evolutionary algorithms. Ecol Inform 3: 46-54.

86.

Whigham PA. 2000. Induction of a marsupial density model using genetic programming and spatial relationships. Ecol Model 131: 299-317.

87.

Whigham PA, Recknagel F. 2001a. An inductive approach to ecological time series modelling by evolutionary computation. Ecol Model 146: 275-287.

88.

Whigham PA, Recknagel F. 2001b. Predicting chlorophyll-a in freshwater lakes by hybridising process-based models and genetic algorithms. Ecol Model 146: 243-251.

89.

Yoo HS. 2002. Statistical analysis of factors affecting the Han River water quality. J Korean Soc Environ Engin 24: 2139-2150.

90.

Zar JH. 1999. Biostatistical Analysis. Prentice-Hall, Upper Saddle River, NJ.

91.

Zuur AF, leno EN, Walker NJ, Saveliev AA, Smith GM. 2009. Limitations of linear regression applied on ecological data. In: Mixed Effects Models and Extensions in Ecology with R (Zurr AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM, eds). Springer, New York, NY, pp 11-33.

Journal of Ecology and Environment