scholarly journals Regional Industrial Water Demand Prediction Based on Improved Series Gray Neural Network

2015 ◽  
Author(s):  
Hu Zhen-Yun ◽  
Chen Zhi-Ming ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Water Demand ◽  
Industrial Water ◽  
Demand Prediction ◽  
Water Demand Prediction ◽  
Industrial Water Demand

Water Demand Forecasting in Hubei Province with BP Neural Network Model

2012 ◽  
Vol 599 ◽  
pp. 701-704
Author(s):  
Zhen Quan Tang ◽  
Gang Liu ◽  
Wen Nian Xu ◽  
Zhen Yao Xia ◽  
Hai Xiao
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Bp Neural Network ◽  
Water Demand ◽  
Demand Forecasting ◽  
Hubei Province ◽  
Demand Prediction ◽  
Water Demand Prediction ◽  
Basic Link ◽  
Bp Neural Network Model

Prediction of water demand is a basic link in water resources plan and management. Reasonable and accurate prediction of storage helps to develop the plan of water resources the next year, which is very favorable to improve the utilization ratio of water resources and reduce the waste of water resources. This paper uses BP neural network to simulate and predict the water content based on the data of water in recent ten years in Hubei province and evaluates the forecast results. The results show that BP neural network for water demand prediction is feasible.

scholarly journals Intelligent Control of Agricultural Irrigation through Water Demand Prediction Based on Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiuyu Bo ◽  
Wuqun Cheng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intelligent Control ◽  
Water Demand ◽  
Accurate Estimation ◽  
Agricultural Irrigation ◽  
Demand Prediction ◽  
Ecological Water Demand ◽  
Water Demand Prediction ◽  
Artificial Neural

In irrigated areas, the intelligent management and scientific decision-making of agricultural irrigation are premised on the accurate estimation of the ecological water demand for different crops under different spatiotemporal conditions. However, the existing estimation methods are blind, slow, or inaccurate, compared with the index values of the water demand collected in real time from irrigated areas. To solve the problem, this paper innovatively introduces the spatiotemporal features of ecological water demand to the forecast of future water demand by integrating an artificial neural network (ANN) for water demand prediction with the prediction indices of water demand. Firstly, the ecological water demand for agricultural irrigation of crops was calculated, and a radial basis function neural network (RBFNN) was constructed for predicting the water demand of agricultural irrigation. On this basis, an intelligent control strategy was presented for agricultural irrigation based on water demand prediction. The structure of the intelligent control system was fully clarified, and the main program was designed in detail. The proposed model was proved effective through experiments.

scholarly journals WATER DEMAND PREDICTION USING ARTIFICIAL NEURAL NETWORK FOR SUPERVISORY CONTROL

2016 ◽  
Vol 36 (1) ◽  
pp. 148-154
Author(s):  
BI Gwaivangmin ◽  
JD Jiya
Keyword(s):  
Neural Network ◽  
Supervisory Control ◽  
Water Demand ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Optimal Operation ◽  
Demand Prediction ◽  
The Neural Network ◽  
Water Demand Prediction

With increase in population growth, industrial development and economic activities over the years, water demand could not be met in a water distribution network.  Thus, water demand forecasting becomes necessary at the demand nodes.  This paper presents Hourly water demand prediction at the demand nodes of a water distribution network using NeuNet Pro 2.3 neural network software and the monitoring and control of water distribution using supervisory control.  The case study is the Laminga Water Treatment Plant and its water distribution network, Jos.  The proposed model will be developed based on historic records of water demand in the 15 selected demand nodes for 60 days, 24 hours run. The data set is categorized into two set, one for training the neural network and the other for testing, with a learning rate of 50 and hidden nodes of 10 of the neural network model.  The prediction results revealed a satisfactory performance of the neural network prediction of the water demand. The predictions are then used for supervisory control to remotely control and monitor the hydraulic parameters of the water demand nodes. The practical application in the plant will cut down the cost of water production and even to a large extend provide optimal operation of the distribution networks solving the perennial problem of water scarcity in Jos. http://dx.doi.org/10.4314/njt.v36i1.19

scholarly journals Economic Determinants of Industrial Water Demand: A Review of the Applied Research Literature

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1684
Author(s):  
Pilar Gracia-de-Rentería ◽  
Ramón Barberán
Keyword(s):  
Water Demand ◽  
Research Literature ◽  
Industrial Water ◽  
Methodological Issues ◽  
Economic Determinants ◽  
Future Studies ◽  
Know How ◽  
Level Of Activity ◽  
Industrial Water Demand ◽  
Available Information

This paper surveys the empirical, economic literature focused on the determinants of industrial water demand. Both the methodological issues and the outcomes of the previous studies are presented and discussed. Attention is given to key methodological issues, such as the available information, the type of data used, the specification of the variables, the choice of the estimated function, its functional form, and the estimation techniques used, highlighting the issues that require greater attention in future studies. Regarding the results, we focus on the estimated elasticities in order to know how the price of water, the level of activity, and the prices of the other inputs influence the demand for water.

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