A Methodology for Forecasting Demands in a Water Distribution Network Based on the Classical and Neural Networks Approach

A Methodology for Forecasting Demands in a Water Distribution Network Based on the Classical and Neural Networks Approach

This paper proposes a three (3)-step methodology to forecast the future water demands of a water distribution network (WDN) composed of ten (10) district metered areas (DMAs). First, pre-processing of the time-series data was performed through outlier elimination, imputation by K-Nearest Neighbors (...

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Bibliographic Details
Main Authors: Yesid Coy, Laura González, Laura Basto, Valeria Rodríguez, Santiago Gómez, Juan Perafán, Simón Cardona, Alejandra Tabares, Juan Saldarriaga
Format: Article
Language:English
Published: MDPI AG 2024-09-01
Series:Engineering Proceedings
Subjects:
water demand
forecasting
DMA
KNN
LSTM
Bayesian optimization
Online Access:https://www.mdpi.com/2673-4591/69/1/29
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