D Methodik

  • Ulrich Frey


Dieses Kapitel stellt die verwendeten Methoden vor. Die drei zum Einsatz kommenden statistischen Verfahren sind multivariate lineare Regressionen, Entscheidungswälder und künstliche neuronale Netzwerke. Die Erfolgsfaktoren werden über die Entwicklung eines Indikatorensystems operationalisiert.

Verwendete Literatur

  1. Agrawal A (2002) Common resources and institutional sustainability. In: Ostrom E, Dietz T, Dolšak N, Stern PC, Stonich S, Weber EU (Hrsg) The drama of the commons. National Academy Press, Washington, S 41–85Google Scholar
  2. Agrawal A, Chhatre A (2006) Explaining success on the commons: Community forest governance in the Indian Himalaya. World Dev 34(1):149–166. CrossRefGoogle Scholar
  3. Alpaydin E (2010) Introduction to machine learning. Adaptive computation and machine learning, 2. Aufl. MIT Press, Cambridge, MassGoogle Scholar
  4. Backhaus K, Erichson B, Plinke W, Weiber R (2008) Multivariate Analysemethoden: Eine anwendungsorientierte Einführung, 12. Aufl. Springer, HeidelbergGoogle Scholar
  5. Backhaus K, Erichson B, Weiber R (2013) Fortgeschrittene multivariate Analysemethoden: Eine anwendungsorientierte Einführung, 2. Aufl. Springer Gabler, BerlinCrossRefGoogle Scholar
  6. Berkes F (1992) Success and failure in marine coastal fisheries of Turkey. In: Bromley DW, Feeny D, Peters P, Gilles JL, Oakerson RJ, Runge CF, Thomson JT (Hrsg) Making the commons work. Institute for Contemporary Studies, San Francisco, S 161–182Google Scholar
  7. Birkhofer K, Diehl E, Andersson J, Ekroos J, Früh-Müller A, Machnikowski F, Mader VL, Nilsson L, Sasaki K, Rundlöf M, Wolters V, Smith HG (2015) Ecosystem services – Current challenges and opportunities for ecological research. Front Ecol Evol 2:413. CrossRefGoogle Scholar
  8. Böhringer C, Jochem PE (2007) Measuring the immeasurable – A survey of sustainability indices. Ecol Econ 63:1–8CrossRefGoogle Scholar
  9. Boyd H, Charles A (2006) Creating community-based indicators to monitor sustainability of local fisheries. Ocean Coast Manage 49(5–6):237–258. CrossRefGoogle Scholar
  10. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. CrossRefGoogle Scholar
  11. Chhatre A, Agrawal A (2008) Forest commons and local enforcement. P Natl Acad Sci USA 105(36):13286–13291. CrossRefGoogle Scholar
  12. Clarke BS, Fokoué E, Zhang HH (2009) Principles and theory for data mining and machine learning. Springer, New YorkCrossRefGoogle Scholar
  13. Freelon D (2013) ReCal OIR: Ordinal interval, and ratio intercoder reliability as a web service. Int J Internet Sci 8(1):10–16Google Scholar
  14. Frey UJ (2016) A synthesis of key factors for sustainability in social–ecological systems. Sustain Sci 29(10):37. Google Scholar
  15. Gentry AH (1988) Changes in plant community diversity and floristic composition on environmental and geographical gradients. Ann Mo Bot Gard 75(1):1–34CrossRefGoogle Scholar
  16. Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 160(3):249–264. CrossRefGoogle Scholar
  17. Jensen K, Hare B, Call J, Tomasello M (2006) What's in it for me? Self-regard precludes altruism and spite in chimpanzees. Proc Biol Sci 273(1589):1013–1021CrossRefPubMedPubMedCentralGoogle Scholar
  18. Khan J, Wei JS, Ringnér M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, Meltzer PS (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7(6):673–679CrossRefPubMedPubMedCentralGoogle Scholar
  19. Knutti R, Stocker TF, Joos F, Plattner G-K (2003) Probabilistic climate change projections using neural networks. Clim Dynam 21:257–272CrossRefGoogle Scholar
  20. Lam MA, Ostrom E (2010) Analyzing the dynamic complexity of development interventions: Lessons from an irrigation experiment in Nepal. Policy Sci 43:1–25CrossRefGoogle Scholar
  21. Lammerts van Bueren EM, Blom EM (1997) Hierarchical framework for the formulation of sustainable forest management standards. Trobenbos Foundation, LeidenGoogle Scholar
  22. Mehrotra K, Mohan CK, Ranka S (1997) Elements of artificial neural networks. MIT Press, Cambridge, MassachusettsGoogle Scholar
  23. Mitchell TM (1997) Machine learning. McGraw-Hill, New YorkGoogle Scholar
  24. Myatt GJ, Johnson WP (2009) A practical guide to data visualization, advanced data mining methods, and applications. Wiley, Hoboken, NJGoogle Scholar
  25. OECD 1994 Environmental indicators: OECD core set. Organisation for Economic Co-operation and Development, ParisGoogle Scholar
  26. Olden JD, Joy MK, Death RG (2004) An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Model 178(3–4):389–397. CrossRefGoogle Scholar
  27. O'Neill MC, Song L (2003) Neural network analysis of lymphoma microarray data: Prognosis and diagnosis near-perfect. BMC Bioinformatics 4(13):1–12Google Scholar
  28. Ostrom E (1990) Governing the commons: The evolution of institutions for collective action. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  29. Ostrom E (2009) A general framework for analyzing sustainability of social-ecological systems. Science 325:419–422CrossRefPubMedGoogle Scholar
  30. Pagdee A, Kim Y-S, Daugherty PJ (2006) What makes community forest management successful: A meta-study from community forests throughout the world. Soc Natur Resour 19:33–52CrossRefGoogle Scholar
  31. Reed RD, Marks RJ (1999) Neural smithing: Supervised learning in feedforward artificial neural networks. MIT Press, Cambridge, MassachusettsGoogle Scholar
  32. Rowley HA, Baluja S, Kanade T (1998) Neural network-based face detection. IEEE Trans PAMI 20(1):23–28CrossRefGoogle Scholar
  33. Sala O, Van Vuuren D, Pereira H, Lodge D, Alder J, Cumming G, Dobson A, Wolters V, Xenopoulos M (2005). Biodiversity across scenarios. In Carpenter S, Pingali P, Bennett E, Zurek M (Hrsg) Ecosystems and human well-being: Scenarios, Bd 2. Island Press, Washington, S 375–440Google Scholar
  34. Salk C, Frey UJ, Rusch H (2014) Comparing forests across climates and biomes: Qualitative assessments, reference forests and regional intercomparisons. PLoS One 9(4):e94800. CrossRefPubMedPubMedCentralGoogle Scholar
  35. Sarle WS (1997) Neural Network FAQ, part 1 of 7: Introduction, periodic posting to the Usenet Zugegriffen: 8. Juli 2012
  36. Soliveres S, Van Der Plas F, Manning P, Prati D, Gossner MM, Renner SC, Alt F, Arndt H, Baumgartner V, Binkenstein J, Birkhofer K, Blaser S, Bluthgen N, Boch S, Bohm S, Borschig C, Buscot F, Diekotter T, Heinze J, Holzel N, Jung K, Klaus VH, Kleinebecker T, Klemmer S, Krauss J, Lange M, Morris EK, Muller J, Oelmann Y, Overmann J, Pasalic E, Rillig MC, Schaefer HM, Schloter M, Schmitt B, Schoning I, Schrumpf M, Sikorski J, Socher SA, Solly EF, Sonnemann I, Sorkau E, Steckel J, Steffan-Dewenter I, Stempfhuber B, Tschapka M, Turke M, Venter PC, Weiner CN, Weisser WW, Werner M, Westphal C, Wilcke W, Wolters V, Wubet T, Wurst S, Fischer M, Allan E (2016) Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536(7617):456–459. CrossRefPubMedGoogle Scholar
  37. Thrush SF, Coco G, Hewitt JE (2008) Complex positive connections between functional groups are revealed by neural network analysis of ecological time series. Am Nat 171(5):669–677. CrossRefPubMedGoogle Scholar
  38. Tucker CM, Randolph JC, Evans T, Andersson KP, Persha L, Green GM (2008) An approach to assess relative degradation in dissimilar forests: Toward a comparative assessment of institutional outcomes. Ecol Soc 13(1):1–21CrossRefGoogle Scholar
  39. Tucker CM (2010) Learning on governance in forest ecosystems: Lessons from recent research. Int J Commons 4(2):687–706CrossRefGoogle Scholar
  40. Waylen KA, Fischer A, McGowan PK, Thirgood SJ, Milner-Gulland EJ (2010) Effect of local cultural context on the success of community-based conservation interventions. Conserv Biol 24(4):1119–1129CrossRefPubMedGoogle Scholar
  41. Widrow B, Rumelhart DE, Lehr ME (1994) Neural networks: Applications in industry, business and science. Commun Acm 37(3):93–105CrossRefGoogle Scholar
  42. Williams G (2011) Data mining with Rattle and R: The art of excavating data for knowledge discovery. Springer, HeidelbergCrossRefGoogle Scholar
  43. Wollenberg EK, Merino L, Agrawal A, Ostrom E (2007) Fourteen years of monitoring community-managed forests: learning from IFRI’s experience. Int For Rev 9(2):670–684Google Scholar
  44. Yeh I-C, Cheng W-L (2010) First and second order sensitivity analysis of MLP. Neurocomputing 73(10–12):2225–2233. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Ulrich Frey
    • 1
  1. 1.Technische ThermodynamikDeutsches Zentrum für Luft- und RaumfahrtStuttgartDeutschland

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