Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 486-490.doi: 10.11896/jsjkx.191200047

• Big Data & Data Science • Previous Articles     Next Articles

BP Neural Network Water Resource Demand Prediction Method Based on Improved Whale Algorithm

MA Chuang, ZHOU Dai-qi, ZHANG Ye   

  1. School of Software Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:MA Chuang,born in 1984,Ph.D,asso-ciate professor.His main researchin-terests include complexnetwork and machine learning.
  • Supported by:
    This work was supported by the Surface Project of National Natural Science Foundation of China (6172099),Program for Innovation Team Buil-ding at Institutions of Higher Education in Chongqing (CXTDG201602010),Science and Technology Innovation Leadership Support Program of Chongqing (CSTCCXLJRC201917),University Outstanding Achievements Transformation Funding Project of Chongqing (KJZH17116),Artificial Intelligence Technology Innovation Important Subject Projects of Chongqing (CSTC2017rgzn-zdyf0140),Innovation and Entrepreneurship Demonstration Team Cultivation Plan of Chongqing(CSTC2017kjrc-cxcytd0063),Industry Important Subject Projects of Chongqing(CSTC2018JSZX-CYZTZX0178,CSTC2018JSZX-CYZTZX0185) and Chongqing Research Program of Basic Research and Frontier Technology (CSTC2017jcyjAX0270,CSTC2018jcyjA0672,CSYC2017jcyjAX0071).

Abstract: With the increasing concentration of modern residential areas and the continuous expansion of water supply network,water supply is facing new difficulties and challenges.It includes the dynamic change of water resource scheduling,the sudden breakdown of pipe network,the uncontrollable loss of water resources,multi-objective and huge calculation.BP neural network has been widely used in water resources prediction because of its strong self-learning ability and generalization ability,but it also has the problems of slow convergence and easy to fall into local extremes.As a kind of optimization algorithm,swarm intelligence algorithm has simple operation,fast convergence speed and strong global optimization ability.In order to improve the convergence speed and prediction accuracy of BP neural network in water resources prediction,a BP neural network water resource demand prediction model based on the optimization of improved whale algorithm is proposed.The optimization breadth and accuracy of the algorithm are strengthened,and then the optimal weights and thresholds output by the improved WOA algorithm are used as initial parameter values to train the model through BP neural network.Through experimental verification,the improved WOA-BP neural network method has better performance in terms of convergence speed and prediction accuracy than the traditional WOA-BP method.

Key words: BP neural network, Water resources demand prediction, Whale optimization algorithm

CLC Number: 

  • TP181
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