Simulation of energy dissipation downstream of labyrinth weirs by applying support vector regression integrated with meta-heuristic algorithms

IF 2.4 3区 环境科学与生态学 Q2 ENGINEERING, CIVIL Journal of Hydro-environment Research Pub Date : 2022-01-01 DOI:10.1016/j.jher.2021.12.003
Amin Mahdavi-Meymand, Wojciech Sulisz
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引用次数: 2

Abstract

In this study, multi-tracker optimization algorithm (MTOA), particle swarm optimization (PSO), and differential evolution (DE) algorithms were integrated with support vector regression (SVR) to predict energy dissipation downstream of labyrinth weirs (ΔE). In order to evaluate the performance of these methods, the results are compared with corresponding outcome obtained by applying two other methods, namely, multilayer perceptron neural network (MLPNN) and multiple linear regressions methods (MLR). The input parameters comprise the discharge, the upstream flow depth, the crest length of a single cycle of the labyrinth weir, the width of a single cycle of the labyrinth weir, the apex width, the number of labyrinth weir cycles, the sidewall angle, and the height of weir. The results indicate that the meta-heuristic algorithms substantially improve the performance of SVR. The results show that the integrative methods, SVR-MTOA, SVR-PSO, and SVR-DE, are more accurate than the MLPNN and the MLR. In average, the integrative methods provide 39.63% more accurate results than the MLPNN and 79.34% more accurate results than the MLR. The average RMSE and R2 for the integrative methods are 0.0054 m and 0.977, respectively. Among all integrative methods, the SVR-MTOA yields the best results, with RMSE = 0.0044 m and R2 = 0.986.

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支持向量回归与元启发式算法相结合模拟迷宫堰下游消能
本研究将多跟踪优化算法(MTOA)、粒子群优化算法(PSO)和差分进化算法(DE)与支持向量回归(SVR)相结合,用于迷宫堰下游能量耗散预测(ΔE)。为了评价这些方法的性能,将结果与另外两种方法,即多层感知器神经网络(multilayer perceptron neural network, MLPNN)和多元线性回归方法(multiple linear regression methods, MLR)得到的结果进行比较。输入参数包括流量、上游水流深度、迷宫堰单周期波峰长度、迷宫堰单周期宽度、迷宫堰顶点宽度、迷宫堰循环数、侧壁角和堰高。结果表明,元启发式算法显著提高了支持向量回归的性能。结果表明,SVR-MTOA、SVR-PSO和SVR-DE综合方法比MLPNN和MLR方法更准确。综合方法的准确率平均比MLPNN高39.63%,比MLR高79.34%。综合方法的平均RMSE和R2分别为0.0054 m和0.977。在所有综合方法中,SVR-MTOA的结果最好,RMSE = 0.0044 m, R2 = 0.986。
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来源期刊
Journal of Hydro-environment Research
Journal of Hydro-environment Research ENGINEERING, CIVIL-ENVIRONMENTAL SCIENCES
CiteScore
5.80
自引率
0.00%
发文量
34
审稿时长
98 days
期刊介绍: The journal aims to provide an international platform for the dissemination of research and engineering applications related to water and hydraulic problems in the Asia-Pacific region. The journal provides a wide distribution at affordable subscription rate, as well as a rapid reviewing and publication time. The journal particularly encourages papers from young researchers. Papers that require extensive language editing, qualify for editorial assistance with American Journal Experts, a Language Editing Company that Elsevier recommends. Authors submitting to this journal are entitled to a 10% discount.
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