A Machine Learning-Based Ensemble Framework for Forecasting PM\({}_{2.5}\) Concentrations in Puli, Taiwan  
[PERSON]  
1 Department of Computer Science and Information Engineering, China University of Technology, Taipei 11695, Taiwan Center for the Conservation of Cultural Heritage, China University of Technology, Taipei 11695, Taiwan Center for the University of California, Santa Cruz University of Technology, Taipei 11  
For long-term examples, the power plants in Taiwan are used to raise their production during summer in response to the high energy demand, and the monsoon usually brings northerly serious air pollutants to Taiwan's west coast in every winter. As short-term examples, the Taiwanese burn a lot of incense and joss papers on some religious ceremony days, many people gather at night for barbecue activities on the Mid-Autumn Festival Day, and anticyclones usually cause the ambient pollutants to last for several days. The diverse methodologies adopted in the literature for PM\({}_{2.5}\) forecasting range from regression and autoregression, machine learning, meta-evolution, receptor model, to hybrid methods. These methods can be classified as parametric or non-parametric ones. The parametric methods require explanatory variables in addition to the PM\({}_{2.5}\) series itself, while the non-parametric methods need only the PM\({}_{2.5}\) series. Parametric methods are effective when the selected explanatory variables are informative to describe the PM\({}_{2.5}\) concentrations. The widely used explanatory variables include wind speed and direction, precipitation, temperature, relative humidity, atmospheric pressure, land use, traffic amount, road types, satellite images, etc. Non-parametric methods analyze the temporal trends or series components in the historical PM\({}_{2.5}\) data and use them as input for predicting the next PM\({}_{2.5}\) concentration. It is worth noting that hybrid methods are currently more popular because each type of methodology has its own strengths and weaknesses and a hybrid of multiple models is likely to complement one another. Several works have shown that hybrid methods can boost the performance obtained by using a single method [7; 8; 9]. This paper proposes an ensemble framework that combines four diverse prediction models. Both ensemble and hybrid methods combine multiple models to enhance the overall performance, however, there is still a difference between them. In the ensemble method, the adopted models work independently with the same input data, and a learning scheme (such as probabilistic, weighting, or voting mechanism) is used to integrate the outputs from the multiple models. While in the hybrid method, the embedded models usually work cooperatively in a one-way fashion. The output of a preceding model is used as the input of the next model. So there is no need to integrate the results of respective models. Our ensemble framework combines four diverse models, namely, the cluster linear regression, Fourier series descriptor, short-term multi-layer perceptron, and long-term multi-layer perceptron. Our ensemble framework includes parametric and non-parametric models, short-term and long-term learning schemes in a single framework. To the best of our knowledge, this paper is the first attempt to develop an ensemble of such diverse models and learning schemes for PM\({}_{2.5}\) forecasting. In addition to the framework design, the feature selection is deliberate. Four types of features, including periodic variables, meteorological variables, short-term meteorological variables, and short-term autoregression variables, are considered. A spiral validation process [10; 11] is devised to reduce the size of the feature set by retaining the most effective variables. We choose a central Taiwan township named Puli as our studied area.