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Application of artificial neural networks in global climate change and ecological research: An overview

  • Review
  • Ecology
  • Published:
Chinese Science Bulletin

Abstract

Fields that employ artificial neural networks (ANNs) have developed and expanded continuously in recent years with the ongoing development of computer technology and artificial intelligence. ANN has been adopted widely and put into practice by researchers in light of increasing concerns over ecological issues such as global warming, frequent El Niño-Southern Oscillation (ENSO) events, and atmospheric circulation anomalies. Limitations exist and there is a potential risk for misuse in that ANN model parameters require typically higher overall sensitivity, and the chosen network structure is generally more dependent upon individual experience. ANNs, however, are relatively accurate when used for short-term predictions; despite global climate change research favoring the effects of interactions as the basis of study and the preference for long-term experimental research. ANNs remain a better choice than many traditional methods when dealing with nonlinear problems, and possesses great potential for the study of global climate change and ecological issues. ANNs can resolve problems that other methods cannot. This is especially true for situations in which measurements are difficult to conduct or when only incomplete data are available. It is anticipated that ANNs will be widely adopted and then further developed for global climate change and ecological research.

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References

  1. Lei X D, Peng C H, Tian D L, et al. Meta-analysis and its application in global change research. Chinese Sci Bull, 2007, 52: 289–302

    Article  Google Scholar 

  2. Hopfield J. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA, 1982, 79: 2554–2558

    Article  Google Scholar 

  3. Jiang C S, Wang C Q, Wei H K, et al. Intelligent Control and Applications (in Chinese). Beijing: Science Press, 2007. 221–222

    Google Scholar 

  4. McClelland J L, Rumelhart D E. Explorations in parallel distributed processing: A handbook of models, programs, and exercises. London: MIT. 1988. 75–77

    Google Scholar 

  5. Paola J D, Schowengerdt R A. A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery. Int J Remote Sens, 1995, 16: 3033–3058

    Article  Google Scholar 

  6. Kavzoglu T, Mather P M. The use of backpropagating artificial neural networks in land cover classification. Int J Remote Sens, 2003, 24: 4907–4938

    Article  Google Scholar 

  7. Mas J F, Flores J J. The application of artificial neural networks to the analysis of remotely sensed data. Int J Remote Sens, 2008, 29: 617–663

    Article  Google Scholar 

  8. Kavzoglu T. Increasing the accuracy of neural network classification using refined training data. Environ Modell Softw, 2009, 24: 850–858

    Article  Google Scholar 

  9. Wang Y N. Intelligence Information Processing Technology (in Chinese). Beijing: Higher Education Press, 2003. 25

    Google Scholar 

  10. Mi X C, Ma K P, Zou Y B. Artificial neural network and its application in agricultural and ecological research (in Chinese). J Plant Ecol, 2005, 29: 863–870

    Google Scholar 

  11. Xiu L N, Liu X N. Current status and future direction of the study on artificial neural network classification processing in remote sensing (in Chinese). Remote Sens Technol Appl, 2003, 18: 339–345

    Google Scholar 

  12. Yu B L, Liu Z Q, Liu H. The hardware and software of ANN (in Chinese). J Zhengzhou Polytech Inst, 2004, 20: 7–8

    Google Scholar 

  13. Chen X, Wu J L, Wang L. Prediction of climate change impacts on streamflow of lake bosten using artificial neural network model (in Chinese). J Lake Sci, 2005, 17: 207–212

    Google Scholar 

  14. Zou Z H, Wang X L. The errors analysis for river water quality prediction based on BP modeling (in Chinese). Acta Sci Circums, 2007, 27: 1038–1042

    Google Scholar 

  15. Holmberg M, Forsius M, Starr M, et al. An application of artificial neural networks to carbon, nitrogen and phosphorus concentrations in three boreal streams and impacts of climate change. Ecol Model, 2006, 195: 51–60

    Article  Google Scholar 

  16. Nour M H, Smith D W, Prepas E E, et al. The application of artificial neural networks to flow and phosphorus dynamics in small streams on the Boreal Plain, with emphasis on the role of wetlands. Ecol Model, 2006, 191: 19–32

    Article  Google Scholar 

  17. Hu C H, Hao Y H, Pang B, et al. Simulation of spring flows from a karst aquifer with an artificial neural network. Hydrol Process, 2008, 22: 596–604

    Article  Google Scholar 

  18. Chen J Y, Adams B J. Integration of artificial neural networks with conceptual models in rainfall-runoff modeling. J Hydrol, 2006, 318: 232–249

    Article  Google Scholar 

  19. Elshorbagy A, Parasuraman K. On the relevance of using artificial neural networks for estimating soil moisture content. J Hydrol, 2008, 362: 1–18

    Article  Google Scholar 

  20. Recknagel F. Ecological Informatics: Overview. In: Jorgensen S E, Fath B, eds. Encyclopedia of Ecology. Oxford: Elsevier, 2008. 1041–1058

    Chapter  Google Scholar 

  21. Chen Y H, Chang F J. Evolutionary artificial neural networks for hydrological systems forecasting. J Hydrol, 2009, 367: 125–137

    Article  Google Scholar 

  22. Lischeid G. Investigating short-term dynamics and long-term trends of SO4 in the runoff of a forested catchment using artificial neural networks. J Hydrol, 2001, 243: 31–42

    Article  Google Scholar 

  23. Kunwar P S, Ankita B, Amrita M, et al. Artificial neural network modeling of the river water quality: A case study. Ecol Model, 2009, 220: 888–895

    Article  Google Scholar 

  24. Park Y S, Régis Céréghino, Arthur C, et al. Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol Model, 2003, 160: 265–280

    Article  Google Scholar 

  25. Song S B, Cai H J. A comprehensive quantitative assessment model for arid area’s basin water soil environment quality (in Chinese). Chin J Appl Ecol, 2005, 16: 345–349

    Google Scholar 

  26. Viotti P, Liuti G, Genov P D. Atmospheric urban pollution: Applications of an artificial neural network (ANN) to the city of Perugia. Ecol Model, 2002, 148: 27–46

    Article  Google Scholar 

  27. Nagendra S M S, Khare M. Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions. Ecol Model, 2006, 190: 99–115

    Article  Google Scholar 

  28. Chen L, Ma G D. Study on wavelet analysis and neural network prediction of SO2 concentration in air (in Chinese). Acta Sci Circum, 2006, 6: 1553–1558

    Google Scholar 

  29. Wei H K. Neural Networks Physical Design Theory and Method (in Chinese). Beijing: National Defense Industry Press, 2005. 105–108

    Google Scholar 

  30. Matthew R, Christoph M, Hong J D, et al. The use of artificial neural networks (ANNs) to simulate N2O emissions from a temperate grassland ecosystem. Ecol Model, 2004, 175: 189–194

    Article  Google Scholar 

  31. Barcenas O P, Olivas E S, Guerrero J D M, et al. Unbiased sensitivity analysis and pruning techniques in neural networks for surface ozone modeling. Ecol Model, 2005, 182: 149–158

    Article  Google Scholar 

  32. He H L, Yu G R, Zhang L M, et al. Simuation CO2 flux of three different ecosystem in ChinaFLUX based on artificial neural network. Sci China Ser D-Earth Sci, 2006, 49(Supp II): 252–261

    Article  Google Scholar 

  33. Melesse A M, Hanley R S. Artificial neural network application for multi-ecosystem carbon flux simulation. Ecol Model, 2005, 189: 305–314

    Article  Google Scholar 

  34. Papale D, Valentini R. A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization. Glob Change Biol, 2003, 9: 525–535

    Article  Google Scholar 

  35. Wang S J, Guan D S. Remote sensing method of forest biomass estimation by artificial neural network models (in Chinese). Ecol Environ, 2007, 16: 108–111

    Google Scholar 

  36. Ito E, Ono K, Ito Y M, et al. A neural network approach to simple prediction of soil nitrification potential: A case study in Japanese temperate forests. Ecol Model, 2008, 219: 200–211

    Article  Google Scholar 

  37. Ostendorf B, Hilbert D W, Hopkins M S. The effect of climate change on tropical rainforest vegetation pattern. Ecol Model, 2001, 145: 211–224

    Article  Google Scholar 

  38. Zhao Z Y, Chow T L, Herb W R, et al. Predict soil texture distributions using an artificial neural network model. Comput Electron Agr, 2009, 65: 36–48

    Article  Google Scholar 

  39. Kavdir I. Discrimination of sunflower, weed and soil by artificial neural networks. Comput Electron Agr, 2004, 44: 153–160

    Article  Google Scholar 

  40. Liu D W, Song K S, Zhang B. Neural network model for leaf chlorophyll content of roadside trees based on hyperspectral approaches (in Chinese). Chin J Ecol, 2006, 25: 238–242

    Google Scholar 

  41. Moisen G G, Frescino T S. Comparing five modelling techniques for predicting forest characteristics. Ecol Model, 2002, 157: 209–225

    Article  Google Scholar 

  42. Hu S P, Yu X X, Wang X P, et al. Application of artificial neural network in optimization of vegetation types in watershed (in Chinese). J Beijing Forest Univ, 2008, 30(Suppl II): 1–5

    Google Scholar 

  43. Ooba M, Hirano T, Mogami J-I, et al. Comparisons of gap-filling methods for carbon flux dataset: A combination of a genetic algorithm and an artificial neural network. Ecol Model, 2006, 198: 473–486

    Article  Google Scholar 

  44. Luo C W, Chen Y, Hu H Q, et al. Neural network grade program of natural forest protection (in Chinese). Chin J Appl Ecol, 2005, 1: 1002–1006

    Google Scholar 

  45. Scrinzi G, Marzullo L, Galvagni D. Development of a neural network model to update forest distribution data for managed alpine stands. Ecol Model, 2007, 206: 331–346

    Article  Google Scholar 

  46. Zhang Z M, Verbeke L P C, Clercq E M D. Uses the artificial intelligence neural network and the DEM data carries on the vegetation change survey (in Chinese). Chinese Sci Bull, 2007, 52(Suppl II): 201–210

    Google Scholar 

  47. Jensen J R, Qiu F, Ji M. Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data. Int J Remote Sens, 1999, 20: 2805–2822

    Article  Google Scholar 

  48. Liu Z H, Chang Y, Chen H W. Estimation of forest volume in Huzhong forest area based on RS, GIS and ANN (in Chinese). Chin J Appl Ecol, 2008, 19: 1891–1896

    Google Scholar 

  49. Wang L H, Xing Y Q. Remote sensing estimation of natural forest biomass on an artificial neural network (in Chinese). Chin J Appl Ecol, 2008, 19: 261–266

    Google Scholar 

  50. Zheng X X, Sun M, Chen Y, et al. Evaluation of regional ecotourism suitability based on GIS and artificial neural network model: A case study of Zhejiang Province (in Chinese). Chin J Ecol, 2006, 25: 1435–1441

    Google Scholar 

  51. Yu Y Y, Guo Z T, Wu H B, et al. Spatial changes in soil organic carbon density and storage of cultivatedsoils in China from 1980 to 2000. Glob Biogeochem Cycles, 2009, 23: GB2021

    Article  Google Scholar 

  52. Campbell W J, Hill S E, Cormp R F. Automatic labeling and characterization of objects using artificial neural networks. Telemat Infoemat, 1989, 6: 259–271

    Article  Google Scholar 

  53. McClelland G E, Dewitt R N, Hemmer T H, et al. Multispectral image-processing with a three-layer back-propagation network. In: Proceedings of International Joint Conference on Neural Networks. New York: IEEE, 1989, 1: 151–153

    Article  Google Scholar 

  54. Hepner G F, Logan T, Ritter N, et al. Artificial neural network classification using a minimal training set: Comparison to conventional supervised classification. Photogram Eng Remote Sens, 1990, 56: 469–473

    Google Scholar 

  55. Downey I D, Power C H, Kanellopoulos I, et al. A performance comparison of Landsat Thematic mapper land cover classification based on neural network techniques and traditional maximum likelihood algorithms and minimum distance algorithms. In: Proceeding of the Annual Conference of the Remote Sensing Society. The Remote Sensing Society: Nottingham, 1992. 518–528

    Google Scholar 

  56. Lin H, Peng C H. Application of Artificial neural network in forest resource management (in Chinese). World Forest Res, 2002, 15: 22–31

    Google Scholar 

  57. Pijanowski B C, Brown D G, Shellito B A, et al. Using neural networks and GIS to forecast land use changes: A land transformation model. Comput Environ Urban Syst, 2002, 26: 553–575

    Article  Google Scholar 

  58. Zhang M Y. Use GIS and ANN to forecast land use change (in Chinese). J Yanshan Univ, 2004, 28: 279–282

    Google Scholar 

  59. Bai M Z, Yue Q L. The predictive system of land use change based on integration the modelbase into GIS (in Chinese). J East China Inst Tech, 2007, 30: 345–349

    Google Scholar 

  60. Lopez G, Rubio M A, Martinez M, et al. Batlles. Estimation of hourly global photosynthetically active radiation using artificial neural network models. Agr Forest Meteorol, 2001, 107: 279–291

    Google Scholar 

  61. Li S C, Wu S H, Dai E F. Assessing the fragility of ecosystem using artificial neural network model (in Chinese). Acta Ecol Sin, 2005, 25: 621–626

    Google Scholar 

  62. Wen Z Y, Cao L P. Modelling of complex ecosystem’s stability with artificial neural network (in Chinese). J Hunan Agr Univ, 2006, 32: 674–678

    Google Scholar 

  63. Li L, Zhang H T. Assessment model of townlet eco-environmental quality based on BP artificial neural network (in Chinese). Chin J Appl Ecol, 2008, 19: 2693–2698

    Google Scholar 

  64. Li H Y, Shi Z, Sha J M, et al. Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques (in Chinese). Chin J Appl Ecol, 2006, 17: 1475–1480

    Google Scholar 

  65. Chen K, Jacobson C, Blong R. Artificial neural networks for risk decision support in natural hazards: A case study of assessing the probability of house survival from bushfires. Environ Model Assess, 2004, 9: 189–199

    Article  Google Scholar 

  66. Jiang W, Feng Z K, Hu L. Forest fire prediction research based on VLBP neural network (in Chinese). Forest Resour Manage, 2007, 1: 95–98

    Google Scholar 

  67. Worner S P, Gevrey M. Modelling global insect pest species assemblages to determine risk of invasion. J Appl Ecol, 2006, 43: 858–867

    Article  Google Scholar 

  68. Fei B H, Guo W, Zang S N, et al. Artificial neural network technology in the application of wood science (in Chinese). Wood Process Mach, 2009, 3: 34–37

    Google Scholar 

  69. Li S C, Zheng D. Applications of artificial neural networks to geosciences: Review and prospect (in Chinese). Adv Earth Sci, 2003, 18: 68–76

    Google Scholar 

  70. Wu H J, Lin Z Y, Guo S L. The application of artificial neural networks in the resources and environment (in Chinese). Resour Enviro Yangtze Basin, 2000, 9: 237–241

    Google Scholar 

  71. Yang Y. Samples complexity estimation algorithm optimizes structure of artificial neural network (in Chinese). J Guang Dong Commun Polytech, 2009, 8: 45–48

    Google Scholar 

  72. Kemp S J, Zaradic P, Hansen F. An approach for determining relative input parameter importance and significance in artificial neural networks. Ecol Model, 2007, 204: 326–334

    Article  Google Scholar 

  73. Gevrey M, Dimopoulos I, Lek S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model, 2003, 160: 249–264

    Article  Google Scholar 

  74. Olden J D, Joy M K, Death R G. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Model, 2004, 178: 389–397

    Article  Google Scholar 

  75. Wang Z L. Artificial Psychology (in Chinese). Beijing: China Machine Press, 2007. 4–6

    Google Scholar 

  76. Li Y C, He C Y. Scenario simulation and forecast of land use/cover in northern China. Chinese Sci Bull, 2008, 53: 1401–1412

    Article  Google Scholar 

  77. D’heygere T, Goethals P L M, Pauw N D. Genetic algorithms for optimisation of predictive ecosystems models based on decision trees and neural networks. Ecol Model, 2006, 195: 20–29

    Article  Google Scholar 

  78. Abramowitz G, Pitman A, Gupta H, et al. Systematic bias in land surface models. J Hydrometeorol, 2007, 8: 989–1101

    Article  Google Scholar 

  79. Geng Y, Dong Y Q, Guo H. Based on artificial neural networks information processing. Project Security, 2009, 6: 50

    Google Scholar 

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Authors and Affiliations

  1. College of Life Science and Technology, Central-South University of Forestry and Technology, Changsha, 410004, China

    ZeLin Liu, ChangHui Peng, WenHua Xiang, DaLun Tian, XiangWen Deng & MeiFang Zhao

  2. Institute of Environment Sciences, Department of Biology Sciences, University of Quebec at Montreal, Case postale 8888, succ Centre-Ville, Montreal, QC, H3C 3P8, Canada

    ChangHui Peng

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  1. ZeLin Liu
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Correspondence to ChangHui Peng.

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Liu, Z., Peng, C., Xiang, W. et al. Application of artificial neural networks in global climate change and ecological research: An overview. Chin. Sci. Bull. 55, 3853–3863 (2010). https://doi.org/10.1007/s11434-010-4183-3

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