17 citations as recorded by crossref.

1. Machine-Learning Models for Sales Time Series Forecasting B. Pavlyshenko https://doi.org/10.3390/data4010015
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3. Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting B. Sahoo et al. https://doi.org/10.1007/s11600-019-00330-1
4. Flood forecasting using prophet and LSTM models: A case study for Sylhet Sadar, Bangladesh M. Rana et al. https://doi.org/10.1016/j.hydrch.2026.100020
5. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale G. Papacharalampous et al. https://doi.org/10.1016/j.advwatres.2019.103470
6. P2D: An Efficient Patient-to-Doctor Framework for Real-Time Health Monitoring and Decision Making G. Saad et al. https://doi.org/10.1109/JSEN.2020.3012432
7. Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting J. Qu et al. https://doi.org/10.1007/s11269-021-02770-1
8. Investigating the Performance of the Informer Model for Streamflow Forecasting N. Tepetidis et al. https://doi.org/10.3390/w16202882
9. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis https://doi.org/10.5194/adgeo-45-201-2018
10. Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling M. Bolick et al. https://doi.org/10.3390/w15142581
11. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. https://doi.org/10.1016/j.jhydrol.2019.123957
12. Towards flexible groundwater-level prediction for adaptive water management: using Facebook’s Prophet forecasting approach H. Aguilera et al. https://doi.org/10.1080/02626667.2019.1651933
13. A minimalistic approach for evapotranspiration estimation using the Prophet model A. Rahman et al. https://doi.org/10.1080/02626667.2020.1787416
14. Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET C. Aditya Satrio et al. https://doi.org/10.1016/j.procs.2021.01.036
15. A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes J. Quilty & J. Adamowski https://doi.org/10.1016/j.envsoft.2020.104718
16. A Framework for Data Prediction and Forecasting in WSN with Auto ARIMA A. Choudhary et al. https://doi.org/10.1007/s11277-021-09237-x
17. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models G. Papacharalampous et al. https://doi.org/10.1016/j.advwatres.2019.103471