1. M. Abadi A. Agarwal P. Barham E. Brevdo Z. Chen C. Citro G.S. Corrado A. Davis J. Dean M. Devin S. Ghemawat I. Goodfellow A. Harp G. Irving M. Isard Y. Jia R. Jozefowicz L. Kaiser M. Kudlur J. Levenberg D. Mane R. Monga S. Moore D. Murray C. Olah M. Schuster J. Shlens B. Steiner I. Sutskever K. Talwar P. Tucker V. Vanhoucke V. Vasudevan F. Viegas O. Vinyals P. Warden M. Wattenberg M. Wicke Y. Yu X. Zheng TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems 2016 https://doi.org/10.48550/arXiv.1603.04467.
2. A novel approach to uncertainty quantification in groundwater table modeling by automated predictive deep learning;Abbaszadeh Shahri;Nat. Resour. Res.,2022
3. A review of uncertainty quantification in deep learning: Techniques, applications and challenges;Abdar;Inf. Fusion,2021
4. Internet of Things (IoT) Architecture for Flood Data Management;Abdul Ghapar;Int. J. Future Gener. Commun. Netw.,2018
5. Neural network modelling of non-linear hydrological relationships;Abrahart;Hydrol. Earth Syst. Sci.,2007