An Ensemble Data-Driven Approach for Enhanced Short-Term Water Demand Forecasting in Urban Areas-Reference-Cited by-同舟云学术

An Ensemble Data-Driven Approach for Enhanced Short-Term Water Demand Forecasting in Urban Areas

Published:2024-09-05 Issue: Volume:73 Page:69
ISSN:
Container-title:The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024)
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Author:

Bakhshipour Amin E.¹,Namdari Hossein²,Koochali Alireza³⁴,Dittmer Ulrich¹,Haghighi Ali¹²⁵^ORCID

Affiliation:

1. Institute of Urban Water Management, RPTU in Kaiserslautern, 67663 Kaiserslautern, Germany

2. Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz 61357-83151, Iran

3. German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany

4. Ingenieurgesellschaft Auto und Verkehr (IAV) GmbH, 10587 Berlin, Germany

5. Sustainable Water Infrastructure Solutions (SWIS) GmbH, 67663 Kaiserslautern, Germany

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/69/1/69/pdf

Reference7 articles.

1. Deep learning–based short-term water demand forecasting in urban areas: A hybrid multichannel model;Namdari;AQUA—Water Infrastruct. Ecosyst. Soc.,2024

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3. Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model;Mu;J. Water Resour. Plann. Manag.,2020

4. NHITS: Neural Hi-erarchical Interpolation for Time Series Forecasting;Challu;AAAI,2023

5. Olivares, K.G., Challú, C., Garza, F., Canseco, M.M., and Dubrawski, A. (2024, March 31). NeuralForecast: User Friendly State-of-the-Art Neural Forecasting Models. Available online: https://github.com/Nixtla/neuralforecast.