Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan, ROC.
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan, ROC.
Sci Total Environ. 2019 Feb 15;651(Pt 1):230-240. doi: 10.1016/j.scitotenv.2018.09.111. Epub 2018 Sep 13.
Air quality deteriorates fast under urbanization in recent decades. Reliable and precise regional multi-step-ahead PM forecasts are crucial and beneficial for mitigating health risks. This work explores a novel framework (MM-SVM) that combines the Multi-output Support Vector Machine (M-SVM) and the Multi-Task Learning (MTL) algorithm for effectively increasing the accuracy of regional multi-step-ahead forecasts through tackling error accumulation and propagation that is commonly encountered in regional forecasting. The Single-output SVM (S-SVM) is implemented as a benchmark. Taipei City of Taiwan is our study area, where three types of air quality monitoring stations are selected to represent areas imposed with high traffic influences, high human activities and commercial trading influences, and less human interventions close to nature situation, respectively. We consider forecasts of PM concentrations as a function of meteorological and air quality factors based on long-term (2010-2016) observational datasets. Firstly, the Kendall tau coefficient is conducted to extract key spatiotemporal factors from regional meteorological and air quality inputs. Secondly, the M-SVM model is trained by the MTL to capture non-linear relationships and share correlation information across related tasks. Lastly, the MM-SVM model is validated using hourly time series of PM concentrations as well as meteorological and air quality datasets. Regarding the applicability of regional multi-step-ahead forecasts, the results demonstrate that the MM-SVM model is much more promising than the S-SVM model because only one forecast model (MM-SVM) is required, instead of constructing a site-specific S-SVM model for each station. Moreover, the forecasts of the MM-SVM are found better consistent with observations than those of any single S-SVM in both training and testing stages. Consequently, the results clearly demonstrate that the MM-SVM model could be recommended as a novel integrative technique for improving the spatiotemporal stability and accuracy of regional multi-step-ahead PM forecasts.
在过去几十年的城市化进程中，空气质量迅速恶化。可靠和精确的区域多步提前 PM 预测对于减轻健康风险至关重要。本研究探索了一种新的框架（MM-SVM），该框架结合了多输出支持向量机（M-SVM）和多任务学习（MTL）算法，通过解决区域预测中常见的误差积累和传播问题，有效地提高了区域多步提前预测的准确性。单输出支持向量机（S-SVM）被用作基准。台湾台北市是我们的研究区域，选择了三种空气质量监测站类型，分别代表受高交通影响、高人类活动和商业交易影响以及接近自然情况的人类干预较少的区域。我们考虑将 PM 浓度预测作为基于长期（2010-2016 年）观测数据集的气象和空气质量因素的函数。首先，进行 Kendall tau 系数分析，从区域气象和空气质量输入中提取关键时空因素。其次，通过 MTL 训练 M-SVM 模型，以捕捉非线性关系并共享相关任务之间的相关信息。最后，使用 PM 浓度的小时时间序列以及气象和空气质量数据集验证 MM-SVM 模型。关于区域多步提前预测的适用性，结果表明，MM-SVM 模型比 S-SVM 模型更有前途，因为只需要一个预测模型（MM-SVM），而不是为每个站点构建特定于站点的 S-SVM 模型。此外，在训练和测试阶段，MM-SVM 的预测结果都比任何单个 S-SVM 的预测结果更符合观测值。因此，结果清楚地表明，MM-SVM 模型可以作为一种新的综合技术，用于提高区域多步提前 PM 预测的时空稳定性和准确性。
