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Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W.,
et al. (2016). An overview of current applications, challenges, and future trends in distributed process-based models in hydrology. Journal of Hydrology. https://doi.org/10.1016/j.
jhydrol.2016.03.026.
Howard, J. H., Kazar, M. L., Menees, S. G., Nichols, D. A., Satyanarayanan, M., Sidebotham, R. N.,
& West, M. J. (1988). Scale and performance in a distributed file system. ACM Transactions on
Computer Systems, 6(1), 51–81. https://doi.org/10.1145/35037.35059.
Intel. (2013). Intel distribution for Apache Hadoop Software.
Jain, T., & Somni, R.. (2013). Intel distribution for Apache Hadoop, 4(3), 530–532. http://www.
intel.com/content/dam/www/public/us/en/documents/articles/intel-distribution-for-apachehadoop-product-brief.pdf.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.
Reed, D. A., & Dongarra, J. (2015). Exascale computing and big data. Communications of the
ACM, 58(7), 56–68. https://doi.org/10.1145/2699414.
Shwartz-Ziv, R., & Tishby, N.. (2017). Opening the black box of deep neural networks via information. ArXiv Preprint ArXiv:1703.00810.
Webster, P.. (2012). Climate change simulation: NASA’s weather supercomputer. CSC World
Spring.
http://www.csc.com/cscworld/publications/81769/81773-supercomputing_the_
climate_nasa_s_big_data_mission.
Computer Science Tools
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik,
1(1), 269–271. https://doi.org/10.1007/BF01386390.
Douglas, D. H., & Peucker, T. K. (1973). Algorithms for the reduction of the number of points
required to represent a digitized line or its caricature. Cartographica: The International
Journal for Geographic Information and Geovisualization, 10(2), 112–122.
Evans, C., Gartrell, A., Gomez, L., Mouyebe, M., Oxley, D., Popuri, S. K., Neerchal, N. K., &
Mehta, A. (2014). Improving the computational efficiency of downscaling GCM data for use
in SWAT.
Frew, J. E., & Dozier, J. (2012). Environmental informatics. Annual Review of Environment and
Resources, 37, 449–472.
Gunturi, Venkata, M. V., Nunes, E., Yang, K. S., & Shekhar, S. (2011). A critical-time-point
approach to all-start-time lagrangian shortest paths: A summary of results. Lecture Notes in
Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics), 6849, 74–91. https://doi.org/10.1007/978-3-642-22922-0_6.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks. https://
doi.org/10.1016/j.neunet.2014.09.003.
Shekhar, S., Evans, M. R., Kang, J. M., & Mohan, P. (2011a). Identifying patterns in spatial information: A survey of methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge
Discovery, 1(3), 193–214. https://doi.org/10.1002/widm.25.
Shekhar, S., Feiner, S. K., & Aref, W. G. (2015a). Spatial computing. Communications of the ACM,
59(1), 72–81. https://doi.org/10.1145/2756547.
Srivastava, J., Desikan, P., & Kumar, V. (2005). Foundations and Advances in Data Mining.
Foundations and advances in data mining, 275–307. https://doi.org/10.1007/b104039.
Tobler, W. R. (1970). A computer movie simulating urban growth in the detroit region. Economic
Geography, 46, 234. https://doi.org/10.2307/143141.
16 Computing