摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员发布了关于支持向量机的新报告。据《中国人民日报陕西消息》报道，NewsRx记者称：“随着能源消耗的快速增长，空气污染问题变得越来越严重。准确检测PM2.5浓度对于人们的健康和生命至关重要。”本研究经费来源于国家自然科学基金(NSFC)。为此，本文提出了基于循环系统优化(CSBO)、变分模式分解(VMD)、Q uantile回归优化的门控回归单元(QRGRU)、山羚优化器(MGO)和最小二乘支持向量机(LSVM)的多因素PM2.5浓度优化预测模型CSBO-VMD-Qrugo-LSVM。其次,提出了基于循环系统优化的变分模式分解算法CSBO-VMD,利用CSBO-VMD将PM2.5浓度自适应分解为有限ED个数的固有模式函数(IMFs),然后采用分位数回归优化的门控递归单元QRGRU,提出了用山羚优化器优化的最小二乘支持向量机MGO-LSVM,利用QRGRU和M GO-LSVM分别对分解分量IMF和最优特征子集进行预测,得到QRGRU和MGO-LSVM的综合预测结果。对QRGRU和MGOLSSVM的预测结果采用均方根逆混合法进行加权,得到最终的预测结果.考虑地理位置、人口密度和污染风险,以北京、沈阳、西安和乌鲁木齐的PM2.5浓度为例,验证了该模型的有效性和通用性.
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Su pport Vector Machines. According to news originating from Shaanxi, People's Repu blic of China, by NewsRx correspondents, research stated, "With the rapid expans ion of increased energy consumption, the issue of air pollution comes to be incr easingly critical. It is essential to achieve accurate PM2.5 concentration predi ction for people's health and lives." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from the Xi'an Universit y of Posts and Telecommunications, "Therefore, a multi-factor PM2.5 concentratio n optimization prediction model based on circulatory system based optimization ( CSBO), variational mode decomposition (VMD), gated recurrent unit optimized by q uantile regression (QRGRU), mountain gazelle optimizer (MGO) and least square su pport vector machine (LSSVM), named CSBO-VMD-QRGRUMGO-LSSVM, is proposed. Firstl y, RFECV is utilized to discover the optimal feature subset with the strongest r elationship with PM2.5 concentration. Secondly, variational mode decomposition o ptimized by circulatory system based optimization, named CSBO-VMD, is proposed. CSBO-VMD is utilized to decompose PM2.5 concentration adaptively into a restrict ed number of intrinsic mode functions (IMFs). Then, gated recurrent unit optimiz ed by quantile regression, named QRGRU, and least squares support vector machine optimized by mountain gazelle optimizer, named MGO-LSSVM, are proposed. The dec omposed components IMFs and optimal feature subsets are predicted by QRGRU and M GO-LSSVM to generate the integrated prediction results of QRGRU and MGO-LSSVM, r espectively. Finally, the prediction results of QRGRU and MGOLSSVM are assigned weights by the inverse root mean square error blending to obtain the final predi ction results. Considering the geographical location, population density and pol lution risk, PM2.5 concentration in Beijing, Shenyang, Xi'an and Urumqi are pred icted to demonstrate the efficiency and universality of the proposed model."
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