中文摘要：

支持向量回归(SVR ) 方法是学习机器算法的一种新奇类型，它很少在多重社会经济的因素下面被用于城市的大气的优秀模型的发展。这研究由作为内核选择线性、光线的基础，花键，和多项式函数介绍四个 SVR 模型，分别地，为城市的灰尘的预言，秋天铺平。模型的输入作为工业煤消费，人口密度，交通流动系数，和购物的密度系数被识别。训练并且测试的结果证明有光线的基础核的 SVR 模型在训练并且测试的过程比另外的三两个更好表演。另外，很多情形分析表明最合适的参数(感觉迟钝的损失功能 ? ，减少错误 C 的影响的参数或参数的平均分发) 是 0.001， 0.5，和 2 000 分别地。

英文摘要：

Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.