文献类型：期刊文献

中文题名：基于遗传算法优化极限学习机模型的干旱预测——以云贵高原为例

英文题名：Drought prediction based on genetic algorithm-optimized extreme learning machine model:case of Yunnan-Guizhou Plateau

作者：刘振男 杜尧 韩幸烨 和鹏飞 周正模 曾天山

第一作者：刘振男

机构：[1]贵州理工学院土木工程学院,贵州贵阳550001;[2]河海大学水文水资源学院,江苏南京210098

第一机构：贵州理工学院土木工程学院

年份：2020

卷号：51

期号：8

起止页码：13-18

中文期刊名：人民长江

外文期刊名：Yangtze River

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；

基金：国家自然科学基金项目(51879069);贵州省科技厅项目(黔科合LH字[2016]7096)。

语种：中文

中文关键词：干旱预测;GA-ELM;SPEI;云贵高原

外文关键词：drought prediction;GA-ELM;SPEI;Yunnan-Guizhou Plateau

摘要：为准确预测干旱情势,提高防旱抗旱能力,构建了遗传算法优化的极限学习机(GA-ELM)模型进行干旱预测。以近年来干旱频发的云贵高原为研究区,利用该模型以关键致旱因子为输入变量实现了云贵高原中长期干旱预测,并与自适应神经模糊推理系统(Adaptive Neural Fuzzy Inference System,ANFIS)模型、极限学习机(Extreme Learning Machine,ELM)模型的预测结果进行比较。结果表明:GA-ELM模型适用于云贵高原地区的干旱预测;与ELM模型相比,不同时间尺度下GA-ELM模型的干旱预测结果精度均有明显提升;在干旱强度和干旱历时方面,GA-ELM模型的预测精度总体上也优于ANFIS模型。
In order to accurately predict drought and improve drought control and relief ability,the genetic algorithm-optimized extreme learning machine model(GA-ELM)was constructed to predict drought.In this paper,key drought-producing factors were used as input variables to realize the medium and long-term drought prediction of the Yunnan-Guizhou Plateau by GA-ELM.Then,the results by GA-ELM was compared with that of adaptive neural fuzzy inference system(ANFIS)model and extreme learning machine(ELM)model.The results showed that the GA-ELM could be applied to drought prediction in Yunnan-Guizhou Plateau.Also,the results obtained by GA-ELM were more accurate than that of ELM at different time scales.The prediction accuracy of GA-ELM was higher than that of ANFIS in terms of drought intensity and drought duration.