Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms
Author:
Affiliation:
1. School of Engineering University of Patras University Campus Patras Greece
Publisher
American Geophysical Union (AGU)
Subject
Water Science and Technology
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021WR030216
Reference113 articles.
1. Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada
2. Untangling hybrid hydrological models with explainable artificial intelligence
3. ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018)
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