Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

Krittakom Srijiranon,Narissara Eiamkanitchat

doi:10.3390/app11146590

Abstract

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

Highlights

Air pollution is a major problem in public health that increases health impacts on both the cardiovascular and respiratory systems in humans [1]
The hourly PM10 class prediction was used in four stations, while the hourly PM2.5 class prediction was used in two stations, due to the reason described earlier
The comparison results of the network transformation (NT) model and the NFT-minimum entropy principle (MEP) model to predict hourly PM10 with three classes of output data are reported in Table 5, where all results were the averaged value between the two testing datasets

Summary

Introduction

Air pollution is a major problem in public health that increases health impacts on both the cardiovascular and respiratory systems in humans [1]. There are many important air pollutants, including ground-level ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM), announced by the World Health Organization. The PM is a mixture of particles that it compounds and four types of components, namely, organic, inorganic, biological, and carbonaceous materials. The size of PM affecting human health has an aerodynamic diameter of less than 10 μm, which can only be detected by an electron microscope. Coarse particulate matter called PM10 is PM with an aerodynamic diameter smaller than 10 μm. There are other types of PM, such as PM1 [7], which are excluded from this research due to air pollution standards

Methods

Results

Conclusion