Combined growth water demand forecasting model based on artificial ecosystem optimization algorithm

被引：0
|
作者：
Cui D. [1 ]
Bao Y. [2 ]
机构：
[1] Wenshan WaterAffairs Bureau of Yunnan Province, Wenshan
[2] Qujing Branch of Yunnan Hydrology and Water Resources Bureau, Qujing
来源：
Water Resources Protection | 2020年 / 36卷 / 06期
关键词：
Artificial ecosystem optimization algorithm; Combined growth model; Richards model; Sparameter optimization; Usher model; Water demand forecast; Weibull model;
D O I：
10.3880/j.issn.1004-6933.2020.06.020
中图分类号：
学科分类号：
摘要：
In order to improve the accuracy of water demand forecasting and expand the application of growth model in water demand forecasting, a combined growth water demand prediction model based on artificial ecosystem optimization ( AEO) algorithm was proposed. Combined with an example, six standard test functions were selected to simulate AEO algorithm in different dimensions, and the simulation results were compared with those of Whale optimization algorithm ( WOA), gray wolf optimization (GWO), teaching optimization ( TLBO) algorithm and traditional particle swarm optimization ( PSO). Based on the combination of three single growth models ( Weibull, Richards, and Usher ), Weibull-Richards-Usher, Weibull-Richards, Weibull-Usher and Richards-Usher were constructed. The AEO algorithm was used to optimize the parameters and weight coefficients of the four combined growth models. The AEO-Weibull-Richards-Usher, AEO-Weibull-Richards, AEO-Weibull-Usher, AEO-Richards-Usher water demand forecasting models were proposed and AEO-Weibull, AEO-Richards, AEO-Usher, AEO-SVM, AEO-BP models were constructed for comparison. Taking the water demand forecast of Shanghai as an example, the combined models were trained and predicted by using the statistical data of the first 30 groups and the last 8 groups. The results show that the optimization accuracy of AEO algorithm is better than that of WOA, GWO, TLBO and PSO algorithms in different dimensions, and has better optimization accuracy and global search ability. The average absolute relative error and the average absolute error of the four combined models are 0． 94% —1． 17% and 30 million-37 million m3, respectively. The prediction accuracy is better than the other five models such as AEO-Weibull. The results show that the AEO algorithm can effectively optimize the parameters and weight coefficients of the combined growth model, and the combined growth model based on AEO algorithm is feasible and effective for water demand prediction. © 2020, Editorial Board of Water Resources Protection. All rights reserved.
引用
收藏
页码：122 / 130
页数：8
相关论文
共 31 条
  • [1] YANG Lina, LI Wenzhu, LIU Xin, On campus interval water demand prediction based on grey genetic BP neural network [ J ], Journal of Water Resources and Water Engineering, 30, 3, pp. 133-138, (2019)
  • [2] GUO Qiang, LI Wenzhu, LIU Xin, Interval water demand prediction method based on bayesian BP network [ J ], Yellow River, 40, 12, pp. 76-80, (2018)
  • [3] CUI Dongwen, Application of genetic neural network model to urban water demand prediction based on theory of phase space reconstruction [ J ], Advances in Science and Technology of Water Resources, 34, 1, pp. 85-89, (2014)
  • [4] SANG Huiru, WAND Lixue, CHEN Shaoming, Et al., Water demand forecast model of RBF neutral networks based on principle component analysis [J], Water Resources and Power, 7, pp. 58-61, (2017)
  • [5] XU Yinshan, LU Sunyun, MEI Yadong, Et al., Application of equi-dimensional information renewal radial basis function neural network model to urban water demand forecast[J], Water Resources Protection, 28, 2, pp. 8-11, (2012)
  • [6] CUI Dongwen, Application of projection pursuit regression model optimized by moth-flame optimization algorithm in prediction of water demand prediction[J], Journal of North China University of Water Resources and Electric Power ( Natural Science Edition), 38, 2, pp. 25-29, (2017)
  • [7] YANG Haoxiang, LIANG Chuan, CUI Ningbo, Prediction of urban water demand by using weighted grey-markov chain model [ J ], Journal of Yangtze River Scientific Research Institute, 32, 7, pp. 15-21, (2015)
  • [8] WANG Pan, LU Baohong, ZHANG Hanwen, Et al., Water demand prediction model based on random forests model and its application[J], Water Resources Protection, 30, 1, pp. 34-37, (2014)
  • [9] HUANG Shuo, PENG Yong, ZHANG Xiaoli, Et al., Application of optimized GM(0,N) model to waterdemand forecast in Dalian City[J], Water Resources and Power, 12, pp. 12-15, (2016)
  • [10] LI Xiaoying, SU Zhiwei, TIAN Jiale, Et al., Research on GRA-MEA-BP coupling model for water demand prediction[ J], Journal of Water Resources and Water Engineering, 29, 1, pp. 50-54, (2018)
1234