Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine
Reference49 articles.
1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., et al. (2015). TensorFlow: Large-scale machine learning on heterogeneous distributed systems. ArXiv:abs/1603.04467
2. Adnan, R. M., Petroselli, A., Heddam, S., Santos, C. A. G., & Kisi, O. (2021). Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model. Stochastic Environmental Research and Risk Assessment, 35(3), 597–616. https://doi.org/10.1007/s00477-020-01910-0
3. Aghelpour, P., Bahrami-Pichaghchi, H., & Varshavian, V. (2021). Hydrological drought forecasting using multi-scalar streamflow drought index, stochastic models and machine learning approaches, in northern Iran. Stochastic Environmental Research and Risk Assessment, 35(8), 1615–1635. https://doi.org/10.1007/s00477-020-01949-z
4. Ardabili, S., Mosavi, A., Dehghani, M., Várkonyi-Kóczy, A. R. (2020). Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review, Cham; pp. 52–62. https://doi.org/10.1007/978-3-030-36841-8_5
5. Bai, P., Liu, X., & Xie, J. (2021). Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models. Journal of Hydrology, 592, 125779. https://doi.org/10.1016/j.jhydrol.2020.125779
Cited by
9 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