Assessment of predictive models for chlorophyll-a concentration of a tropical lake

BMC Bioinformatics

Volumn 12, Issue SUPPL. 13, 2011, Pages

  Malek, Sorayya ^a   Syed Ahmad, Sharifah M ^b   Singh, Sarinder Kaur K ^a   Milow, Pozi ^a   Salleh, Aishah ^a  

^a UNIVERSITY OF MALAYA   (Malaysia)

^b UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGAL BIOMASS; AMMONIA NITROGEN; BINARY CLASSIFICATION; CHLOROPHYLL A; CHLOROPHYLL-A CONCENTRATION; CORRELATION COEFFICIENT; DATA SET; ECOLOGICAL MODELS; ERROR OF PREDICTIONS; HYBRID EVOLUTIONARY ALGORITHM; LAKE SAND RESERVOIRS; MALAYSIA; MESOTROPHIC; MULTIPLE LINEAR REGRESSIONS; NITRATE NITROGEN; PERFORMANCE CRITERION; PREDICTIVE MODELS; RECEIVING OPERATING CHARACTERISTICS; RECURRENT ARTIFICIAL NEURAL NETWORKS; ROOT MEAN SQUARE ERRORS; SECCHI DEPTH; STEPWISE PROCEDURE; TRAINING AND TESTING; TROPHIC STATUS; TROPICAL LAKES; WATER TEMPERATURES; WATERBODIES; AQUATIC ECOSYSTEM; DATA SETS;

EUTROPHICATION; FUZZY LOGIC; GENETIC ALGORITHMS; LAKES; MEAN SQUARE ERROR; NEURAL NETWORKS; NITROGEN; RESERVOIRS (WATER); TROPICS; CHLOROPHYLL;

CHLOROPHYLL; BIOINFORMATICS;

ALGAE;

CHLOROPHYLL; NITROGEN;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; ECOSYSTEM; EUTROPHICATION; LAKE; MALAYSIA; MICROBIOLOGY; STATISTICAL MODEL;

CHLOROPHYLL; ECOSYSTEM; EUTROPHICATION; LAKES; LINEAR MODELS; MALAYSIA; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); NITROGEN;

EID: 84864043326     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-S13-S12     Document Type: Article

References (55)

1. NAHRIM A desktop Study on the Status of Lake Eutrophication in Malaysia, Final Report. 2005, Malaysia, NAHRIM.
2. Zati Z. National Overview: The Status of Lakes Eutrophication in Malaysia. Colloquium on Lakes and Reservoir Management: Status and Issues 2007, Putrajaya, Malaysia.
3. Ryding S, Rast W. The Control of Eutrophication of lakes and Reservoirs. 1989, Parthenon Publishing Co., UNESCO.
4. Chapman D. Water Quality Assessments A guide to the use of biota, sediments and water in environmental monitoring. 1996, 59-126. UNESCO/WHO/UNEP, E & FN SPON, 2.
5. Padisák J. Phytoplankton. In The Lakes Handbook Volume 1 Limnology and Limnetic Ecology. 2004, 251-308. Blackwell Publishing, O'Sullivan PE, Reynolds CS.
6. Lee JHW, Huang Y, Dickman M, Jayawardena AW. Neural network modelling of coastal algal blooms. Ecol.Model 2003, 159:179-201.
7. Hou GX, Song LR, Liu JT, Xiao BD, Liu YD. Modeling of Cyanobacterial blooms in Hypereutrophic Lake Dianchi, China. J. Freshwat. Ecol 2004, 19:623-629.
8. Melesse AM, Krisnaswamy J, Zhang KQ. Modeling coastal eutrophication at Florida Bay using neural networks. J. Coast. Res 2008, 24('B):190-196.
9. Zeng Y, Yang ZF, Liu JF. Prediction of the concentration of chlorophyll- alpha for Liuhai urban lakes in Beijing City. J Environ Sci (China) 2006, 18(4):827-831.
10. Karul C, Soyupak S, Cilesiz AF, Akbay N, Germen E. Case studies on the use of neural networks in eutrophication modelling. Ecol Model 2000, 134(2-3):145-152.
11. Soyupak S, Chen DG. Fuzzy logic model to estimate seasonal pseudo steady state chlorophyll- a concentrations in reservoirs. Environ Monit Assess 2004, 9:51-59.
12. Recknagel F, Petzoldt T, Jaeke O, Krusche F. Hybrid expert system DELAQUA-a toolkit for water quality control of lakes and reservoirs. Ecol Model 1994, 71:17-36.
13. Chen Q. A robust fuzzy logic approach to modelling algal biomass. J Hydraulics Res 2004, 42(3):303-309.
14. Chen Q, Mynett AE. Integration of data mining techniques with heuristic knowledge in a fuzzy logic modelling of eutrophication in Taihu Lake. Ecol Model 2003, 162:55-67.
15. Cao H, Recknagel F, Joob GJ, Kim DK. Discovery of predictive rule sets for chlorophyll-a dynamics in the Nakdong River (Korea) by means of the hybrid evolutionary algorithm HEA. Ecol Inform 2006, 43-53.
16. Cao H, Recknagel F, Welk A, Kim B, Takamura N. Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication. Ecol Inform 2006, 330-342. Springer-Verlag Berlin, Heidelberg, New York, 2.
17. Recknagel F, Welk A, Cao H, Chan WS, Talib A. Rule-based agents for forecasting algal population dynamics in freshwater lakes discovered by hybrid evolutionary algorithms. Ecol Inform 2008, 46-54.
18. Smith S. A retrospective Look at the Effects of Phosphorus Removal. Inland Waters, USEPA, Office of Water Regulations and Standards 1981, Washington, DC, EPA-440/5-81-010.
19. Canfield DC, Cooler DE, Haller WT, Watkins CE, Maceina MJ. Predictions of chlorophyll-a concentrations in Florida lakes: Importance of aquatic macrophytes. Can. Jour. Fish Aquatic Science 1984, 41:409-501.
20. Flach P, Blockeel H, Ferri C, Hernandez-Orallo J, Struyf J. Decision support for data mining: Introduction to ROC analysis and its applications. Data mining and decision support: Aspects of integration and collaboration 2003, 81-90.
21. Egan JP. Signal detection theory and ROC analysis. 1975, Academic Press, New York.
22. Murtaugh PA. The Statistical Evaluation of Ecological Indicators. Ecol Appl 1996, 6(1):132-139.
23. Putrajaya Corporation Wetlands Plants. 2000, Putrajaya Corporation, Selangor Darul Ehsan, Putrajaya Corporation.
24. WHO UNEP/WHO/UNESCO/WMO Project on Global Environmental Monitoring. GEM Water Operational Guide 1987, WHO.
25. American Public Health Association (APHA) Standard methods for the examination of water and waste water. 1995, American Water Works Association (AWWA) and Water Environment Federation APHA, Washington, DC, 19, American Public Health Association (APHA).
26. Haan CT. Statistical Methods in Hydrology. 1977, The Iowa State University Press.
27. van den Boogaard HFP, Mynett AE, Ali M. Self organisation feature maps for the analysis of hydrological and ecological data sets. Proceedings of Hydroinformatics 1998, Babovic, V, Larsen LC.
28. Maier HR, Dandy GC. Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications. Environ Modell Softw 2000, 15:101-124.
29. Lachtermacher, Fuller JD. Backpropagation in hydrological time series forecasting. Stochastic and Statistical Methods in Hydrology and Environmental Engineering 1994, 3:229-242. Time Series Analysis in Hydrology and Environmental Engineering, Hipel KW, MacLeod AI, Panu US, Singh VP.
30. Principe JC, Euliano NR, Lefebvre WC. Neural and Adaptive Systems: Fundamentals through Simulations. 1999, 672. John Wiley & Sons.
31. Wang XJ, Liu RM. Spatial analysis and eutrophication assessment for chlorophyll a in Taihu Lake. Environ Monit Assess 2005, 101:167-174. Hill.
32. Li M, Ju Y, Sunil K, Thomas JS. Modeling potential habitats for alien species Dreissena polymorpha in Continental USA. Acta Ecologica Sinica 2008, 28(9):4253-4258.
33. Pineda F. Generalization of backpropagation to recurrent neural networks. Phys Rev Lett 1987, 19(59):2229-2232.
34. Cybenko G. Approximation by superposition of sigmoidal functions. Math Control Syst 1989, 2:303-314.
35. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. Parallel Distributed Processing 1986, 318-362. MIT Press, Rumelhart DE, McClelland JL.
36. Principe JC, Euliano NR, Lefebvre WC. Neural and Adaptive Systems: Fundamentals through Simulations. 1999, John Wiley & Sons.
37. Thomann RV, Mueller JA. Principles of Surface Water Quality Modeling and Control. 1987, Harper and Row, New York.
38. Smith AE, Mason AK. Cost estimation predictive modeling: Regression versus neural network. The Engineering Economist 1997, 42(2):137-161.
39. Shen Q, Chouchoulas A. FuREAP: a fuzzy-rough estimator of algae populations. Artif Intelligence Eng 2001, 15:13-24.
40. Kohonen T. Self organised formation of topological correct feature maps. Biol Cybernetics 1982, 43:59-69.
41. Ross TJ. Fuzzy Logic with Engineering Applications. 1994, McGraw-Hill, New York.
42. MacQueen J. Some methods for classification and analysis of multivariable observation. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability 1967, 1:281-297. Berkeley University of California Press.
43. Chiu SL. Fuzzy Model Identification Based on Cluster Estimation. J Intel Fuzzy Syst 1994, 2:267-278.
44. Price KP, Pyke DA, Mendes L. Shrub dieback in a semiarid ecosystem: the integration of remote sensing and geographic informatics systems for detecting vegetation change. Photogrammeng Rem Eng Rem S 1992, 58:455-463.
45. Unni KS, Pawar S. The phytoplankton along a pollution gradient in the river Mahanadi (M.P. state) India - a multivariate approach. Hydrobiologia 2000, 430:87-96.
46. Iriarte A, Purdie DA. Factors controlling the timing of major spring bloom events in an UK south coast estuary. Estuar. Coast. Shelf Sci 2004, 61:679-690.
47. Shapiro J. Current beliefs regarding dominance by bluegreens: the case for the importance of CO2 and pH. Verh int Ver Limnol 1990, 24:38-54.
48. Reynolds CS, Wiseman SW, Clarke MJO. Growth- and loss-rate responses of phytoplankton to intermittent artificial mixing and their application to the control of planktonic algal biomass. J Appl Ecol 1984, 21:11-39.
49. Campbell KL, Edwards DR. Phosphorus and water quality impacts. Agricultural Nonpoint Source Pollution:Watershed Management and Hydrology 2001, 91-108. Lewis Publishers, Boca Raton, F, Ritter WF Shirmohammadi A.
50. Moss B. The influence of environmental factors on the distribution of freshwater algae: an experimental study. I. Introduction and influence of calcium concentration. J Ecol 1973, 61:157-177.
51. O'Brien WJ, Hershey AE, Hobbie JE, Hullar MA, Kipphut GW, Miller MC, Moller B, Vestal JR. Control mechanisms of arctic lake ecosystems: a limnocorral experiment. Hydrobiol 1992, 240:143-188.
52. Kammat ND. Topics in Algae. 1982, 250. Print Aids, Aurangabad 1, P.G. Atre.
53. Jrgensen SE. Fundamentals of Ecological Modelling. 1994, 520-527. Elsevier, Amsterdam, 2.
54. Salski A. Fuzzy knowledge-based models in ecological research. Ecol Model 1992, 63:103-112.
55. Wehr JD, Descy JP. Use of phytoplankton in large river management. J Phycol 1998, 34:741-749.