Table of Contents Author Guidelines Submit a Manuscript
Volume 2017 (2017), Article ID 3418145, 10 pages
Research Article

Development of ANN Model for Wind Speed Prediction as a Support for Early Warning System

1Department of Construction Management and Technology, Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia
2Department of Hydraulic Engineering and Geotechnical Engineering, Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia

Correspondence should be addressed to Ivana Sušanj

Received 28 September 2017; Accepted 28 November 2017; Published 20 December 2017

Academic Editor: Milos Knezevic

Copyright © 2017 Ivan Marović et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. De Bono, B. Chatenoux, C. Herold, and P. Peduzzi, Global Assessment Report on Disaster Risk Reduction 2013: From Shared Risk to Shared Decision Support for Risk Management: Design of EWS 13 Value-The Business Case for Disaster Risk Reduction, UNISDR, Geneva, Switzerland, 2013.
  2. Harvard Humanitarian Initiative et al. Disaster Relief 2.0, “The future of information sharing in humanitarian emergencies,” HHI; United Nations Foundation; OCHA; The Vodafone Foundation, Washington DC, USA, 2010.
  3. J. C. Gaillard and J. Mercer, “From knowledge to action: Bridging gaps in disaster risk reduction,” Progress in Human Geography, vol. 37, no. 1, pp. 93–114, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Weichselgartner and R. Kasperson, “Barriers in the science-policy-practice interface: Toward a knowledge-action-system in global environmental change research,” Global Environmental Change, vol. 20, no. 2, pp. 266–277, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. E. L. Quarantelli, “Disaster crisis management: a summary of research findings,” Journal of Management Studies, vol. 25, no. 4, pp. 373–385, 1988. View at Publisher · View at Google Scholar · View at Scopus
  6. UN/ISDR platform for the promotion of early warning (PPEW) and un Secretairat of the International strategy for disaster reduction (UN/ISDR), “Developing early warning system: A checklist,” in Proceedings of the EWC III Third International Conference on Early Warning: From Concept to Action, Bonn, Germany, March, 2006.
  7. L. Lin, J. T. Eriksson, H. Vihriala, and L. Soderlund, “Predicting wind behavior with neural networks,” in Proceedings of the 1996 European Wind Energy Conference, pp. 655–658, Goteborg, Sweden, May 1996.
  8. M. A. Mohandes, S. Rehman, and T. O. Halawani, “A neural networks approach for wind speed prediction,” Journal of Renewable Energy, vol. 13, no. 3, pp. 345–354, 1998. View at Publisher · View at Google Scholar · View at Scopus
  9. M. C. Alexiadis, P. S. Dokopoulos, H. S. Sahsamanoglou, and I. M. Manousaridis, “Short-term forecasting of wind speed and related electrical power,” Solar Energy, vol. 63, no. 1, pp. 61–68, 1998. View at Publisher · View at Google Scholar · View at Scopus
  10. P. M. Fonte, G. X. Silva, and J. C. Quadrado, “Wind speed prediction using artificial neural networks,” WSEAS Transactions on Systems, vol. 4, no. 4, pp. 379–383, 2005. View at Google Scholar · View at Scopus
  11. M. Monfared, H. Rastegar, and H. M. Kojabadi, “A new strategy for wind speed forecasting using artificial intelligent methods,” Journal of Renewable Energy, vol. 34, no. 3, pp. 845–848, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. S. A. Pourmousavi Kani and M. M. Ardehali, “Very short-term wind speed prediction: a new artificial neural network-Markov chain model,” Energy Conversion and Management, vol. 52, no. 1, pp. 738–745, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. Q. Zhou, T. Xiong, M. Wang, C. Xiang, and Q. Xu, “Diagnosis and early warning of wind turbine faults based on cluster analysis theory and modified ANFIS,” Energies, vol. 10, no. 7, article 898, 2017. View at Publisher · View at Google Scholar
  14. I. Sušanj, N. Ožanić, and I. Marović, “Methodology for developing hydrological models based on an artificial neural network to establish an early warning system in small catchments,” Advances in Meteorology, vol. 2016, Article ID 9125219, 14 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. I. Sušanj, Development of the hydrological rainfall-runoff model based on artificial neural network in small catchments [Ph.D. thesis], University of Rijeka, Faculty of Civil Engineering, Rijeka, Croatia, 2017.
  16. A. Asemi, A. Safari, and A. Asemi Zavareh, “The role of management information system (MIS) and decision support system (DSS) for manager’s decision making process,” International Journal of Business and Management, vol. 6, no. 7, pp. 164–173, 2011. View at Publisher · View at Google Scholar
  17. I. Marovic, Decision support system in real estate value management [Ph.D. thesis], University of Zagreb, Faculty of Civil Engineering, Zagreb, Croatia, 2013.
  18. N. Jajac, Design of decision support systems in the management of infrastructure systems of the urban environment, [M.S. thesis], University of Split, Faculty of Economics, Split, Croatia, 2007.
  19. N. Jajac, S. Knezic, I. Marovic, S. Knezić, and I. Marović, “Decision support system to urban infrastructure maintenance management,” Organization, Technology & Managament in Construction, vol. 1, no. 2, pp. 72–79, 2009. View at Google Scholar
  20. E. Turban, Decision Support and Expert Systems: Management Support Systems, Macmillan Publishing Company, New York, NY, USA, 1993.
  21. M. H. Glantz, Usable Science 8: Early Warning Systems: Do’s and Don’ts, Report of Workshop, Shanghai, China, October 2003.
  22. Millennium Ecosystem Assessment, Ecosystems and Human Well-Being: A Framework for Assessment, Island Press, Washington, DC, USA, 2003.
  23. P. A. Schrodt and D. J. Gerner, “The impact of early warning on institutional responses to complex humanitarian crises,” in Proceedings of the Third Pan-European International Relations Conference and Joint Meeting with the International Studies Association, Vienna, Austria, September 1998.
  24. P. Hall, “Early warning systems: Reframing the discussion,” Australian Journal of Emergency Management, vol. 22, no. 2, 2007. View at Google Scholar
  25. UN/ISDR International Strategy for Disaster Reduction, “Hyogo framework for action 2005–2015: building the resilience of nations and communities to disasters,” in Proceedings of the World Conference on Disaster Reduction, Hyogo, Japan, January, 2005.
  26. T. Comes, B. Mayag, and E. Negre, “Decision support for disaster risk management: integrating vulnerabilities into early-warning systems,” in Proceedings of the International Conference on Information Systems for Crisis Response and Management in Mediterranean Countries, pp. 178–191, Springer International Publishing, Cham, Germany, October 2014.
  27. V. V. Krzhizhanovskaya, G. S. Shirshov, and N. B. Melnikova, “Flood early warning system: design, implementation and computational modules,” Procedia Computer Science, vol. 4, pp. 106–115, 2011. View at Publisher · View at Google Scholar
  28. H. R. Maier, A. Jain, G. C. Dandy, and K. P. Sudheer, “Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions,” Environmental Modeling and Software, vol. 25, no. 8, pp. 891–909, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. H. B. Demuth and M. H. Beale, Neural Network ToolboxTM User's Guide, The MathWorks, INC., 2004.
  30. M. T. Hagan, H. B. Demuth, M. H. Beale, O. De Jes, and O. De Jesús, Neural Network Design (20), PWS Publishing Company, Boston, Mass, USA, 1996.
  31. R. Abrahart, P. E. Kneale, and L. M. See, Neural Networks for Hydrological Modelling, Taylor & Francis Group, London, UK, 2004.
  32. P. Matić, Short-term forecasting of hydrological inflow by use of the artificial neural networks [Ph.D. thesis], University of Split, Faculty of Electrical Engineering, Mechanical Engineering And Naval Architecture, Split, Croatia, 2014.
  33. S. Grbac, L. Malnar, A. Vorkapić, D. Car-Pušić, and I. Marović, ““Preliminary analysis of the spatial attributes affecting students' quality of life at the University of Rijeka,” in Proceedings of the People, Buildings and Environment 2016, an International Scientific Conference, vol. 4, pp. 109–117, Luhačovice, Czech Republic, 2016.