1. 新疆水利水电科学研究院
2. 四川水发勘测设计研究有限公司
纸质出版:2024
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毛建刚, 王庆杰. 群智能算法在月径流预测支持向量机建模中的适应性研究[J]. 水电能源科学, 2024,(5):34-38.
毛建刚, 王庆杰. 群智能算法在月径流预测支持向量机建模中的适应性研究[J]. 2024, (5): 34-38.
毛建刚, 王庆杰. 群智能算法在月径流预测支持向量机建模中的适应性研究[J]. 水电能源科学, 2024,(5):34-38. DOI: 10.20040/j.cnki.1000-7709.2024.20230929.
毛建刚, 王庆杰. 群智能算法在月径流预测支持向量机建模中的适应性研究[J]. 2024, (5): 34-38. DOI: 10.20040/j.cnki.1000-7709.2024.20230929.
变化环境下,径流的精准预测愈加困难,集成稳健的优化算法是近年来机器学习发展的动力,也是提高径流预测精度的有效途径。为规避支持向量机(SVM)建模时适应度函数选取的不足,提出基于群智能算法的SVM参数优化范式,并以新疆克孜尔水库月平均径流量预测为例,对粒子群算法(PSO)、差分算法(DE)、灰狼算法(GWO)、鲸鱼算法(WOA)和麻雀算法(SSA)5个典型算法进行仿真验证。结果表明,PSO建立SVM模型合格率(QR)小于60%
预测精度不达标,其余4类算法的平均绝对相对误差和纳什系数分别介于10%~20%和0.75~1之间,预测效果良好;20次独立运算结果中,PSO、WOA、SSA和DE存在预测结果较差的情况,其中PSO的稳定性最差。综合而言,GWO优化的SVM(SVMGWO)在月径流预测中精度、稳定性和可靠性更佳。
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