Short-term demand forecast using a bank of neural network models trained using genetic algorithms for the optimal management of drinking water networks | Citedby Results | Journal of Hydroinformatics | IWA Publishing

Skip to Main Content

Skip Nav Destination

Short-term demand forecast using a bank of neural network models trained using genetic algorithms for the optimal management of drinking water networks

Journal of Hydroinformatics (2017) 19 (1): 1–16.

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

Short term water demand forecast modelling using artificial intelligence for smart water management

M Kavya
Aneesh Mathew
Padala Raja Shekar
Sarwesh P

Sustainable Cities and Society (2023) 95: 104610.

Water Requirement in North China from Grey Point Prediction and Grey Interval Prediction

Lifeng Wu
Yuan Meng

Water (2023) 15 (8): 1453.

Short-Term Bathwater Demand Forecasting for Shared Shower Rooms in Smart Campuses Using Machine Learning Methods

Ganggang Zhang
Yingbin Hu
Dongxuan Yang
Lei Ma
Mengqi Zhang
Xinliang Liu

Water (2022) 14 (8): 1291.

A Machine-Learning Pipeline for Large-Scale Power-Quality Forecasting in the Mexican Distribution Grid

Juan J. Flores
Jose L. Garcia-Nava
Jose R. Cedeno Gonzalez
Victor M. Tellez
Felix Calderon
Arturo Medrano

Applied Sciences (2022) 12 (17): 8423.

Comparison of EEMD-ARIMA, EEMD-BP and EEMD-SVM algorithms for predicting the hourly urban water consumption

Xingpo Liu
Yiqing Zhang
Qichen Zhang

Journal of Hydroinformatics (2022) 24 (3): 535.

Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control

Christian Kühnert
Naga Mamatha Gonuguntla
Helene Krieg
Dimitri Nowak
Jorge A. Thomas

Water (2021) 13 (5): 644.

An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: a case study

Alireza Goli
Hasan Khademi-Zare
Reza Tavakkoli-Moghaddam
Ahmad Sadeghieh
Mazyar Sasanian
Ramina Malekalipour Kordestanizadeh

Network: Computation in Neural Systems (2021) 32 (1): 1.

Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea

Kang-Min Koo
Kuk-Heon Han
Kyung-Soo Jun
Gyumin Lee
Jung-Sik Kim
Kyung-Taek Yum

Sustainability (2021) 13 (11): 6056.

Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals

Oluwaseun Oyebode
Damilola E. Babatunde
Chukwuka G. Monyei
Olubayo M. Babatunde

Heliyon (2019) 5 (11): e02796.

Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real‐Time Application

Jinduan Chen
Dominic L. Boccelli

Water Resources Research (2018) 54 (2): 879.

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

Rodrigo Lopez Farias
Vicenç Puig
Hector Rodriguez Rangel
Juan Flores

Energies (2018) 11 (3): 660.

Wind speed time series reconstruction using a hybrid neural genetic approach

H Rodriguez
J J Flores
V Puig
L Morales
A Guerra
F Calderon

IOP Conference Series: Earth and Environmental Science (2017) 93: 012020.

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption

Hua’an Wu
Bo Zeng
Meng Zhou

International Journal of Environmental Research and Public Health (2017) 14 (11): 1386.