The use of neural networks in agroecological modelling

Computers and Electronics in Agriculture

Volumn 18, Issue 2-3, 1997, Pages 73-90

  Schultz, A ^a   Wieland, R ^a  

^a LEIBNIZ CENTRE FOR AGRICULTURAL LANDSCAPE RESEARCH ZALF   (Germany)

Author keywords

Agroecology; Ecology; Fuzzy modelling; Landscape modelling; Modelling; Neural network models; Uncertainty

Indexed keywords

COMPUTER SIMULATION; ECOLOGY; ECOSYSTEMS; FUZZY SETS; NEURAL NETWORKS;

AGROECOLOGY;

AGRICULTURE;

EID: 0031212622     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0168-1699(97)00021-5     Document Type: Article

References (18)

1. Dawson, M.S., Amar, F., Rawat, V., Fung, A.K., Manry, M.T. 1994. Land Use Classification Using Fast Learning Neural Networks and Topology Selection Algorithms. 1994 International Geoscience and Remote Sensing Symposium CD-Rom, http://stargate.jpl.nasa.gov:1083/cgi-bin.
2. Desjardins, R.L., Schuepp, P.H., MacPherson, J.I. 1997. Ground Truthing Using Aircraft-based Flux Measurements. Proc. Int. Symp. Appl. Agrometeorol. Agroclimatol., 24-26 April 1996, Volos, Greece, Luxembourg: Office for Official Publications of the European Communities.
3. Hertz, J., Krogh, A., Palmer, R.G., 1995. Introduction to the Theory of Neural Computation. Addison-Wesley, Reading, MA, pp. 327.
4. Hoffmann, J., Wieland, R. 1996. Neuro-fuzzy-Modellierung als ein Werkzeug zur Nutzung von Wildpflanzen in Kulturlandschaften als Bioindikatoren für Klimaänderungen am Beispiel von Carex humilis und Lycopodium annotinum. Bundesanstalt für Landbauforschung, Völkenrode, Germany.
5. Joergensen, S.E. 1994. Fundamentals of Ecological Modelling. Elsevier, Amsterdam, p. 628.
6. Kaule, G. 1991. Arten- und Biotopschutz. Ulmer, Stuttgart, pp. 519.
7. Kimes, D., Levine, E. 1996. Using Artificial Neural Networks for Prediction of Soil Carbon Dynamics. Third International Conference/Workshop on Integrating GIS and Environmental Modelling CD-Rom, http://www.ncgia.ucsb.edu/conf/Santa_Fe_CD-Rom
8. Peschel, M. 1995. Der Taoismus in Religion und Wissenschaft. Seminar Systemwissenschaften Rep. 9, Technical University Chemnitz, Chemnitz, Germany.
9. Ripley, B.D. 1994. Statistical aspects of neural networks. In: O.E. Barndorff-Nielsen, J.E. Jensen, W.S. Kendall (Editors), Networks and Chaos - Statistical and Probabilistic Aspects, Chapman and Hall, London, pp. 40-123.
10. Sarle, W.S. 1994. Neural Networks and Statistical Models. Proc. 19th Ann. SAS Users Group Int. Conf., Dallas, Texas, pp. 1538-1550.
11. Schultz, A., Mirschel., W. 1994. Modelle auf dem Prüfstand - Wie genau sind agrarökologische Simulationsmodelle? Z. f. Agrarinformatik 2: 22-29.
12. Schultz, A. 1997. Informationsbedarf, Komplexität und Genauigkeit von landschaftsbezogenen Simulationsmodellen. Arch. Nature Conser. Landscape Res. (in press).
13. Souyris, J.C., Wang, L., Hsu, C.C., Kong, J.A. 1994. Inversion of soil moisture with radar backscattering data. 1994 International Geoscience and Remote Sensing Symposium CD-Rom, http://stargate.jpl.nasa.gov:1083/cgi-bin.
14. Tae-Soo, C., Young, S.P., Kyong, H.M., Eui, Y.C., 1996. Patternizing communities by using an artificial neural network. Ecol. Modelling 90, 69-78.
15. Traeger, H. 1994. Einführung in die Fuzzytheorie. B.G. Teubner, Stuttgart, pp. 176.
16. Wenkel, K.-O., Mirschel, W. 1995. Agroökosystemmodellierung - Grundlage für die Abschätzung von Auswirkungen möglicher Landnutzungs- und Klimaänderungen. ZALF-Bericht 24, ZALF, Müncheberg, Germany.
17. Wieland, R., Schultz, A., Lutze, G., 1995. Habitatmodelle im Rahmen der dynamischen Landschaftsmodellierung. Wissenschaftliche Berichte des Forschungszentrums Karlsruhe 5622, 95-105.
18. Wilkins, M.F., Boddy, L., Morris, C.W., 1996. A comparison of some neural and non-neural methods for identification of phytoplankton from flow cytometry data. Comput. Appl. Biosci. 12, 9-18.