Environmental Informatics: Advancing Data Intensive Sciences to Solve Environmental Problems

  • Chaowei Yang
  • Yan Xu
  • Daniel Fay


The 21st Century witnesses emergence of geospatial cyberinfrastructure and other relevant geospatial technologies (Yang et al., 2010) for collecting data, extracting information, simulating phenomena scenarios, and supporting decision making (Caragea et al., 2005; Stadler et al., 2006). The advancements of the geospatial technologies not only provide great opportunities for us to better understand environmental issues and better position us to solve global to local environmental problems (Pecar-Ilic and Ruzic, 2006), but also pose great challenges for us to handle terabytes to petabytes of heterogeneous environmental data. Environmental informatics (Green and Klomp, 1998; Hilty, Page and Hrebí < ¡ek, 2006) should be revisited to efficiently and effectively manage, integrate, and mine information and knowledge from the vast amount of data for supporting environmental decisions (Hey, Tansley and Tolle, 2008).


Cloud Computing Ozone Concentration High Performance Computing Open Geospatial Consortium South Florida Water Management District 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Barros, A.P. (2005, Jul. 31-Aug.4). Environmental informatics - Long-lead flood forecasting using Bayesian neural networks. Paper presented at the International Joint Conference on Neural Networks, Montreal, Canada.Google Scholar
  2. Caragea, D., Zhang, J., Bao, J., Pathak, J. and Honavar, V. (2005). Algorithms and software for collaborative discovery from autonomous, semantically heterogeneous, distributed information sources. Lecture Notes in Computer Science, 3734, 13-44.CrossRefGoogle Scholar
  3. Chen, Z., Gangopadhyay, A., Karabatis, G., McGuire, M. and Welty, C. (2007). Semantic integration and knowledge discovery for environmental research. Journal of Database Management, 18, 43-68.CrossRefGoogle Scholar
  4. Devarakonda, R., Palanisamy, G., Green, J.M. and Wilson, B.E. (2010). Data sharing and retrieval using OAI-PMH. Earth Science Informatics, 3, 1-5.CrossRefGoogle Scholar
  5. Dhanushkodi, S.R., Mahinpey, N., Srinivasan, A. and Wilson, M. (2008). Life cycle analysis of fuel cell technology. Journal of Environmental Informatics, 11, 36-44.CrossRefGoogle Scholar
  6. Goodchild M., Yuan, M. and Cova, T.J. (2007). Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science, 21, 239-260.CrossRefGoogle Scholar
  7. Green, D.G. and Klomp, N.I. (1998). Environmental informatics—A new paradigm for coping with complexity in nature. Complexity International, 6. Google Scholar
  8. Gruiz, K. (2009). Web-based information system and decision support tool: The structure and use of the MOKKA IT tool. Land Contamination and Reclamation, 17, 695702.Google Scholar
  9. Hey, T., Tansley, S. and Tolle, K. (2009). The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Press, Redmond, WA.Google Scholar
  10. Hilty, L.M., Page, B. and Hrebitfek, J. (2006). Environmental informatics. Environmental Modelling and Software, 21, 1517-1518.CrossRefGoogle Scholar
  11. Karatzas, K., Nikolaou, K. and Moussiopoulos, N. (2004). Timely and valid air quality information: The APNEE-TU Project. Fresenius Environmental Bulletin, 13, 874878.Google Scholar
  12. Kalapanidas, E. and Avouris, N. (2003). Feature selection for air quality forecasting: A genetic algorithm approach. AI Communications, 16, 235-251.Google Scholar
  13. MacDonell, M., Morgan, K. and Newland, L. (2002). Integrating information for better environmental decisions. Environmental Science and Pollution Research, 9, 359-368.CrossRefGoogle Scholar
  14. Mayfield, C., Joliat, M. and Cowan, D. (2001). The roles of community networks in environmental monitoring and environmental informatics. Advances in Environmental Research, 5, 385-393.CrossRefGoogle Scholar
  15. Pecar-Ilic, J. and Ruzic, I. (2006). Application of GIS and Web technologies for Danube waterway data management in Croatia. Environmental Modelling and Software, 21, 1562-1571.CrossRefGoogle Scholar
  16. Pillmann, W., Geiger, W. and Voigt, K. (2006). Survey of environmental informatics in Europe. Environmental Modelling and Software, 21, 1519-1527.CrossRefGoogle Scholar
  17. Radermacher, F.J., Riekert, W.-F., Page, B. and Hilty, L.M. (1994). Trends in environmental information processing. IFIP Transactions. A: Computer Science and Technology (A-52), 597-604.Google Scholar
  18. Rasuly, A., Naghdifar, R. and Rasoli, M. (2010). Detecting of Arasbaran forest changes applying image processing procedures and GIS Techniques. Procedía Environmental Sciences, 2, 454-464.CrossRefGoogle Scholar
  19. Stadler, M., Ahlers, D., Bekker, R.M., Finke, J., Kunzmann, D. and Sonnenschein, M. (2006). Web-based tools for data analysis and quality assurance on a life-history trait database of plants of Northwest Europe. Environmental Modelling and Software, 21, 1536-1543.CrossRefGoogle Scholar
  20. Slini, T., Karatzas, K. and Moussiopoulos, N. (2003). Correlation of air pollution and meteorological data using neural networks. International Journal of Environment and Pollution, 20, 218-229.Google Scholar
  21. Stockwell, D.R.B., Beach, J.H., Stewart, A., Vorontsov, G., Vieglais, D. and Pereira, R.S. (2006). The use of the GARP genetic algorithm and Internet grid computing in the Lifemapper world atlas of species biodiversity. Ecological Modelling, 195, 139-145.CrossRefGoogle Scholar
  22. Tochtermann, K. and Maurer, H. (2000). Knowledge Management and Environmental Informatics. Journal of Universal Computer Science, 6, 517-536.Google Scholar
  23. Wang, X. (2007). Environmental informatics for environmental planning and management. Journal of Environmental Informatics, 9, 1-3.CrossRefGoogle Scholar
  24. Xie, J., Yang, C., Zhou, B. and Huang, Q. (2010). High performance computing for the simulation of dust storms. Computers, Environment, and Urban Systems, 34, 278-290.CrossRefGoogle Scholar
  25. Yang, C., Goodchild, M., Huang, Q., Nebert, D., Raskin, R., Xu, Y., Fay, D. and Bambacus, M. (2011a). Spatial Cloud Computing - How geospatial science use and help to shape cloud computing. International Journal of Digital Earth, 4, 305329.Google Scholar
  26. Yang, C., Raskin, R., Goodchild, M.F. and Gahegan, M. (2010). Geospatial Cyberinfrastructure: Past, Present and Future. Computers, Environment, and Urban Systems, 34, 264-277.CrossRefGoogle Scholar
  27. Yang, C., Wu, H., Huang, Q., Li, Z. and Li, J. (2011b). Utilizing spatial principles to optimize distributed computing for enabling physical science discoveries. Proceedings of National Academy of Sciences, doi: /10.1073/pnas.0909315108.Google Scholar
  28. Zhizhin, M., Kihn, E., Redmon, R., Poyda, A., Mishin, D., Medvedev, D. et al. (2007). Integrating and mining distributed environmental archives on Grids. Concurrency Computation Practice and Experience, 19, 2157-2170.CrossRefGoogle Scholar

Copyright information

© Capital Publishing Company 2011

Authors and Affiliations

  • Chaowei Yang
    • 1
  • Yan Xu
    • 2
  • Daniel Fay
    • 2
  1. 1.Center for Intelligent Spatial Computing, and Department of Geography and GeoInformation SciencesGeorge Mason UniversityFairfaxUSA
  2. 2.Earth, Energy and Environment at Microsoft Research Connections Microsoft CorporationRedmondUSA

Personalised recommendations