Publisher
Springer Science and Business Media LLC
Subject
Computers in Earth Sciences,Statistics, Probability and Uncertainty,General Agricultural and Biological Sciences,General Environmental Science
Reference36 articles.
1. Addor N, Nearing G, Prieto C, Newman AJ, Le Vine N, Clark MP (2018) Selection of hydrological signatures for large-sample hydrology. Earth arXiv: 12 Feb 2018 Web
2. Aggarwal CC (2018) Neural networks and deep learning. Springer 10:978–983
3. Anctil F, Perrin C, Andréassian V (2004) Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall–runoff forecasting models. Environ Model Softw 19:357–368
4. Ayzel G (2019) Does deep learning advance hourly runoff predictions? In: Sergey I Smagin, Alexander A Zatsarinnyy (eds): 5th International conference information technologies and high-performance computing (ITHPC-2019), Khabarovsk, Russia: CEUR Workshop Proceedings
5. Biondi D, Freni G, Iacobellis V, Mascaro G, Montanari A (2012) Validation of hydrological models: conceptual basis, methodological approaches and a proposal for a code of practice. Phys Chem Earth Parts A/B/C 42:70–76
Cited by
44 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献