Cycle reservoir with regular jumps for forecasting ozone concentrations: two real cases from the east of Croatia

Satisfying the national air quality standards represents a challenge nowadays for developing countries. Air pollution in industrial cities is one of the foremost problems that affect human health and might cause loss of human life. One of the main attributes that can cause a significant impact on people’s health is the ground-level ozone pollution. Ozone can raise the ratio of asthma attacks, permanent damage to lungs, and maybe death. Forecasting its concentration levels is essential for planning well-designed environment protection strategies. In this paper, a state-space reservoir model called cycle reservoir with jumps (CRJ) is used to predict the level of ozone concentrations in the east of Croatia utilizing some meteorological parameters including the temperature, relative humidity, wind speed, wind direction, and the pollutants PM10. CRJ is a particular type of recurrent neural networks with powerful performance when applied for complex temporal problems. Two cases from the east of Croatia are investigated in this work: the Kopaćki Rit area and the Osijek city. The proposed CRJ model shows superiority of CRJ model in forecasting ozone concentrations compared to linear regression, multilayer perceptron (MLP) and radial basis function (RBF) network.