Environmental Informatics: Advancing Data Intensive Sciences to Solve Environmental Problems
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).
KeywordsCloud Computing Ozone Concentration High Performance Computing Open Geospatial Consortium South Florida Water Management District
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- 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
- Green, D.G. and Klomp, N.I. (1998). Environmental informatics—A new paradigm for coping with complexity in nature. Complexity International, 6. Google Scholar
- 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
- Hey, T., Tansley, S. and Tolle, K. (2009). The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Press, Redmond, WA.Google Scholar
- 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
- Kalapanidas, E. and Avouris, N. (2003). Feature selection for air quality forecasting: A genetic algorithm approach. AI Communications, 16, 235-251.Google Scholar
- 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
- 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
- Tochtermann, K. and Maurer, H. (2000). Knowledge Management and Environmental Informatics. Journal of Universal Computer Science, 6, 517-536.Google Scholar
- 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
- 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