Machine Learning Model for Battle of Water Demand Forecasting-Reference-Cited by-同舟云学术

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Machine Learning Model for Battle of Water Demand Forecasting

Published:2024-09-03 Issue: Volume:140 Page:37
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:

Pagano Mario¹,Santonastaso Giovanni Francesco¹^ORCID,Di Nardo Armando¹,Cuomo Salvatore²^ORCID,Schiano Di Cola Vincenzo³^ORCID

Affiliation:

1. Dipartimento di Ingegneria, Università della Campania Luigi Vanvitelli, via Roma 29, 81031 Aversa, Italy

2. Dipartimento di Matematica e Applicazioni “R. Caccioppoli”, Università degli Studi Federico II, via Vicinale dell’Infermeria 58, 80125 Napoli, Italy

3. Cyberneid srl, 80122 Naples, Italy

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/69/1/37/pdf

Reference3 articles.

1. Pu, Z., Yan, J., Chen, L., Li, Z., Tian, W., and Tao, T. (2023). A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting. Front. Environ. Sci. Eng., 17.

2. Urban Water Demand Forecasting: Review of Methods and Models;Donkor;J. Water Resour. Plan. Manag.,2014

3. Extremely randomized trees;Geurts;Mach. Learn.,2006

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