scholarly journals Large metropolitan water demand forecasting using DAN2, FTDNN, and KNN models: A case study of the city of Tehran, Iran

2016 ◽  
Vol 14 (6) ◽  
pp. 655-659 ◽  
Author(s):  
M. Ghiassi ◽  
F. Fa'al ◽  
A. Abrishamchi
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Water Demand Forecasting ◽  
The City

scholarly journals Variable selection methods for water demand forecasting in Ethiopia: Case study Gondar town

2018 ◽  
Vol 4 (1) ◽  
pp. 1537067 ◽  
Author(s):  
Mohammed Gedefaw ◽  
Wang Hao ◽  
Yan Denghua ◽  
Abel Girma ◽  
Mustafa Ibrahim Khamis
Keyword(s):  
Variable Selection ◽  
Water Demand ◽  
Demand Forecasting ◽  
Selection Methods ◽  
Water Demand Forecasting ◽  
Gondar Town

scholarly journals Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study

2019 ◽  
Vol 1284 ◽  
pp. 012004 ◽  
Author(s):  
Leandro L Lorente-Leyva ◽  
Jairo F Pavón-Valencia ◽  
Yakcleem Montero-Santos ◽  
Israel D Herrera-Granda ◽  
Erick P Herrera-Granda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Artificial Neural

scholarly journals Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona Case Study.

2014 ◽  
Vol 47 (3) ◽  
pp. 10457-10462 ◽  
Author(s):  
Ajay Kumar Sampathirao ◽  
Juan Manuel Grosso ◽  
Pantelis Sopasakis ◽  
Carlos Ocampo-Martinez ◽  
Alberto Bemporad ◽  
...  
Keyword(s):  
Drinking Water ◽  
Water Demand ◽  
Large Scale ◽  
Demand Forecasting ◽  
Optimal Operation ◽  
Water Networks ◽  
Water Demand Forecasting

Artificial Neural Networks for Bottled Water Demand Forecasting: A Small Business Case Study

2019 ◽  
pp. 362-373 ◽  
Author(s):  
Israel D. Herrera-Granda ◽  
Joselyn A. Chicaiza-Ipiales ◽  
Erick P. Herrera-Granda ◽  
Leandro L. Lorente-Leyva ◽  
Jorge A. Caraguay-Procel ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Small Business ◽  
Water Demand ◽  
Demand Forecasting ◽  
Business Case ◽  
Bottled Water ◽  
Water Demand Forecasting ◽  
Business Case Study

Short-Term Water Demand Forecasting: A Case Study

2008 ◽  
Author(s):  
JingJing Zhang ◽  
Rengao Song ◽  
Nageshwar R. Bhaskar ◽  
Mark N. French
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Short Term ◽  
Water Demand Forecasting

Research on BP Neural Network Model for Water Demand Forecasting and its Application

2012 ◽  
Vol 170-173 ◽  
pp. 2352-2355 ◽  
Author(s):  
Yue Feng Sun ◽  
Hao Tian Chang ◽  
Zheng Jian Miao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Water Demand ◽  
Demand Forecasting ◽  
Water Demand Forecasting ◽  
Trial And Error Method ◽  
Bp Neural Network Model ◽  
Resources Planning ◽  
Error Method

It is difficult to determine a proper neurons number of the mid-layer when using the BP neural network for water demand forecasting. Aiming at the problem, the BP neural network is presented in this paper for water demand forecasting. A suitable neurons number in the mid-layer is calculated based on the empirical formula method and trial and error method. A certain basin in China is taken as a case study. The results indicate that the mean relative error is 2.42%. The water consumption is 42.8 billion m3 in 2015 and 43.6 billion m3 in 2030 in the study area. The results are useful for water resources planning and management.

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