Hybrid intelligent system for air quality forecasting using phase adjustment
The pollution caused by particulate matter (PM) concentration has a negative impact on population health, due to its relationship with several diseases. In this sense, several intelligent systems have been proposed for forecasting the PM concentration. Although it is known in the literature that PM concentration time series behave like random walk, to the authors knowledge there is no intelligent systems developed to forecast the PM concentration that consider this characteristic. In this paper, we present an architecture developed to forecast time series guided by random walk process. The architecture, called Time-delay Added Evolutionary Forecasting (TAEF), consists of two steps: parameters optimization and phase adjustment. In the first step, a genetic optimization procedure is employed to adjust the parameters of a Multilayer Perceptron neural network that is used as the prediction model. The genetic algorithm adjusts the following parameters of the prediction model: the number of input nodes (time lags), the number of neurons in the hidden layer and the training algorithm. The second step is performed aiming to reduce the difference between the forecasting and the actual concentration value of the time series, that occur in the forecasting of the time series with random walk behavior. The approach is data-driven and only uses the past values of the pollutant concentrations to predict the next day concentration; in other words, it does not require any exogenous information. The experimental study is performed using time series of concentration levels of particulate matter (PM2.5 and PM10) from Helsinki and shows that the approach overcomes previous state-of-the-art methods by a large margin.
air quality forecasting time series genetic algorithm pm2.5 pm10 artificial neural network