publication . Preprint . 2017

A trans-disciplinary review of deep learning research for water resources scientists

Shen, Chaopeng;
Open Access English
  • Published: 06 Dec 2017
Deep learning (DL), a new-generation of artificial neural network research, has transformed industries, daily lives and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as inter-disciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with...
free text keywords: Statistics - Machine Learning, Computer Science - Machine Learning
Related Organizations
Download from
122 references, page 1 of 9

Abramowitz, G., H. Gupta, A. Pitman, Y. Wang, R. Leuning, H. Cleugh, K. Hsu, G. Abramowitz, H. Gupta, A. Pitman, Y. Wang, R. Leuning, H. Cleugh, and K. Hsu (2006), Neural Error Regression Diagnosis (NERD): A Tool for Model Bias Identification and Prognostic Data Assimilation, J. Hydrometeorol., doi:10.1175/JHM479.1.

Abramowitz, G., A. Pitman, H. Gupta, E. Kowalczyk, Y. Wang, G. Abramowitz, A. Pitman, H. Gupta, E. Kowalczyk, and Y. Wang (2007), Systematic Bias in Land Surface Models, J. Hydrometeorol., doi:10.1175/JHM628.1.

Aires, F., C. Prigent, W. B. Rossow, and M. Rothstein (2001), A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations, J. Geophys. Res. Atmos., 106(D14), 14887-14907, doi:10.1029/2001JD900085. [OpenAIRE]

Akaike, H. (1974), A new look at the statistical model identification, IEEE Trans. Automat. Contr., 19(6), 716-723, doi:10.1109/TAC.1974.1100705. [OpenAIRE]

Alipanahi, B., A. Delong, M. T. Weirauch, and B. J. Frey (2015), Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning, Nat. Biotechnol., 33(8), 831-838, doi:10.1038/nbt.3300.

Altae-Tran, H., B. Ramsundar, A. S. Pappu, and V. Pande (2017), Low Data Drug Discovery with One-Shot Learning, ACS Cent. Sci., 3(4), 283-293, doi:10.1021/acscentsci.6b00367.

Angermueller, C., T. Pärnamaa, L. Parts, and O. Stegle (2016), Deep learning for computational biology., Mol. Syst. Biol., 12(7), 878, doi:10.15252/MSB.20156651. [OpenAIRE]

Arpit, D., S. Jastrzębski, N. Ballas, D. Krueger, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio, and S. Lacoste-Julien (2017), A Closer Look at Memorization in Deep Networks, in Proceedings of the 34 th International Conference on Machine Learning, Sydney, Australia, PMLR 70.

ATLAS, C. (2012), A particle consistent with the Higgs boson observed with the ATLAS detector at the Large Hadron Collider., Science, 338(6114), 1576-82, doi:10.1126/science.1232005.

Aurisano, A., A. Radovic, D. Rocco, A. Himmel, M. D. Messier, E. Niner, G. Pawloski, F. Psihas, A. Sousa, and P. Vahle (2016), A convolutional neural network neutrino event classifier, J. Instrum., 11(9), P09001-P09001, doi:10.1088/1748- 0221/11/09/P09001. [OpenAIRE]

Bach, S., A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek (2015), On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation, edited by O. D. Suarez, PLoS One, 10(7), e0130140, doi:10.1371/journal.pone.0130140.

Bai, Y., Z. Chen, J. Xie, and C. Li (2016), Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models, J. Hydrol., 532, 193-206, doi:10.1016/J.JHYDROL.2015.11.011.

Baldassi, C., C. Borgs, J. T. Chayes, A. Ingrosso, C. Lucibello, L. Saglietti, and R. Zecchina (2016), Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes., Proc. Natl. Acad. Sci. U. S. A., 113(48), E7655-E7662, doi:10.1073/pnas.1608103113. [OpenAIRE]

Baldi, P., and P. Sadowski (2014), The dropout learning algorithm, Artif. Intell., 210, 78- 122, doi:10.1016/J.ARTINT.2014.02.004.

Baldi, P., P. Sadowski, and D. Whiteson (2014), Searching for exotic particles in highenergy physics with deep learning, Nat. Commun., 5, doi:10.1038/ncomms5308. [OpenAIRE]

122 references, page 1 of 9
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue