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

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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Water Demand Forecasting in Hubei Province with BP Neural Network Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.599.701 ◽

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.

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City Water Demand Forecasting Based on Improved BP Neural Network

Journal of Residuals Science and Technology ◽

10.12783/issn.1544-8053/14/s1/15 ◽

2017 ◽

Vol 14 (S1) ◽

pp. S111-S117 ◽

Cited By ~ 1

Author(s):

Ying Xing ◽

Zhenwei You ◽

Bo Zhang ◽

Xiaoguang Zhou ◽

Ludi Wang ◽

...

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Water Demand ◽

Demand Forecasting ◽

Water Demand Forecasting ◽

City Water

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Regional logistics demand forecasting: a BP neural network approach

Complex & Intelligent Systems ◽

10.1007/s40747-021-00297-x ◽

2021 ◽

Author(s):

Lijuan Huang ◽

Guojie Xie ◽

Wende Zhao ◽

Yan Gu ◽

Yi Huang

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Demand Forecasting ◽

Indicator System ◽

Regional Logistics ◽

Logistics Enterprises ◽

Logistics Demand ◽

Bp Neural Network Model

AbstractWith the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

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