Predicting time-series for water demand in the big data environment using statistical methods, machine learning and the novel analog methodology dynamic time scan forecasting | Citedby Results | Water Supply | IWA Publishing

Skip to Main Content

Skip Nav Destination

Predicting time-series for water demand in the big data environment using statistical methods, machine learning and the novel analog methodology dynamic time scan forecasting

Water Supply (2023) 23 (2): 624–644.

This article has been cited by the following articles in journals that are participating in CrossRef Cited-by Linking.

Medium and long-term regional water demand prediction using Harris hawks optimisation–backpropagation neural network model

Mengzhuo Yang
Erkun Gao
Gaoxu Wang
Daiyuan Li
Wenqi Zhou
Xingchi Zhou

Scientific Reports (2024) 14 (1)

Chinese Water Demand Forecast Based on iTransformer Model

Zhi-Wei Tian
Ru-Liang Qian

IEEE Access (2024) 12: 115853.

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

Maria Drogkoula
Konstantinos Kokkinos
Nicholas Samaras

Applied Sciences (2023) 13 (22): 12147.

Application of a data-driven DTSF and benchmark models for the prediction of electricity prices in Brazil: A time-series case

Tiago Silveira Gontijo
Rodrigo Barbosa de Santis
Marcelo Azevedo Costa

Journal of Renewable and Sustainable Energy (2023) 15 (3)