16 citations as recorded by crossref.

1. Machine-Learning Models for Sales Time Series Forecasting B. Pavlyshenko
2. Short-term water demand forecasting using data-centric machine learning approaches G. Liu et al.
3. Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting B. Sahoo et al.
4. 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.
5. P2D: An Efficient Patient-to-Doctor Framework for Real-Time Health Monitoring and Decision Making G. Saad et al.
6. Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting J. Qu et al.
7. Investigating the Performance of the Informer Model for Streamflow Forecasting N. Tepetidis et al.
8. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis
9. Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling M. Bolick et al.
10. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al.
11. Towards flexible groundwater-level prediction for adaptive water management: using Facebook’s Prophet forecasting approach H. Aguilera et al.
12. A minimalistic approach for evapotranspiration estimation using the Prophet model A. Rahman et al.
13. Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET C. Aditya Satrio et al.
14. A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes J. Quilty & J. Adamowski
15. A Framework for Data Prediction and Forecasting in WSN with Auto ARIMA A. Choudhary et al.
16. 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.