Prediction of Status Particulate Matter 2.5 using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO
Air pollution is the entry or inclusion of living things, energy substances, and other components into the air. Moreover, Air pollution is the presence of one or several contaminants in the outside atmospheric air such as dust, foam, gas, fog, smoke or steam in large quantities with various properties and time intervals of the contaminants in the air resulting in disturbances to the lives of humans, plants or animals. One of the parameters measured in determining air quality is PM2.5. However, PM2.5 has a higher probability of being able to enter the lower respiratory tract because small particle diameters can potentially pass through the lower respiratory tract. In this paper, we will get two different insight. First, the probability of status change using Markov chain and second, forecasting by using VAR-NN-PSO(Vector Autoregressive, Neural Network, Particle Swarm optimization). More details we classify by three classifications no risk (1-30), medium risk (30-48), and moderate (>49) in Pingtung and Chaozhou start from January 2014 to May 2019. This data is starting from January 2014 to May 2019 so that it can be modeled with the Markov chain. At the same time, we perform Hybrid VAR-NN-PSO to forecast PM2.5 in Pingtung and Chaozhou. In this optimization, the search for solutions is carried out by a population consisting of several particles. Based on the results of the discussion, opportunities for the transition from monthly status change are obtained continuous stochastic time with a stationary probability distribution. Regarding the VAR-NN-PSO, we obtained the mean absolute percentage error (MAPE) 3.57% for PM2.5 data in Pingtung and 4.87% for PM2.5 data in Chaozhou, respectively. This model can be predicted to forecasting 180 days ahead. The population in PSO has generated randomly with the smallest value and the largest value the accuracy.
The Multidisciplinary Open Access Journal, Vol. 7, No. 1, 2019
Rezzy Eko Caraka, Rung Ching Chen, Toni Toharudin, Bens Pardamean, Hasbi Yasin, Shih-Hung Wu
