Information Technologies in Environmental Engineering
(From the back cover) Information technologies have evolved to an enabling science for natural resource management and conservation, environmental engineering, scientific simulation and integrated assessment studies. Computing plays a significant role in every day practices of environmental engineers, natural scientists, economists, and social scientists. The complexity of natural phenomena requires interdisciplinary approaches, where computing science offers the infrastructure for environmental data collection and management, scientific simulations, decision support documentation and reporting. Ecology, environmental engineering and natural resource management comprise an excellent real-world testbed for IT system demonstration, while raising new challenges for computer science. Complexity, uncertainty and scaling issues of natural systems form a demanding application domain for sensor networks and earth observation systems; modelling, simulation and scientific workflows, data management and reporting, decision support and intelligent systems, distributed computing environments, geographical information systems, heterogeneous systems integration, software engineering, accounting systems and control systems. This book is the result of the 4th International ICSC Symposium on Information Technologies in Environmental Engineering (ITEE-2009). Recent success stories in ecoinformatics, promising ideas and new challenges are discussed among computer scientists, environmental engineers, economists and social scientists, who showcase new computing paradigms for environmental problem solving and decision making.
I. N. Athanasiadis, P. A. Mitkas, A. E. Rizzoli, J. Marx Gomez (eds.), Information Technologies in Environmental Engineering, ITEE-2009, 2009, Springer Verlag, doi:10.1007/978-3-540-88351-7.
You might also enjoy (View all publications)
- Learning long-term crop management strategies with CyclesGym
- Rapid turnover of sensor data to genetic evaluation for dairy cows in the cloud
- Mixing process-based and data-driven approaches in yield prediction