Air quality prediction in Uberlândia, Brazil, using linear models and neural networks
نویسندگان
چکیده
Particulate air pollution is associated with a range of effects on human health, including effects on the respiratory and cardiovascular systems, asthma and mortality. Hence, the development of an efficient forecasting and early warning system for providing air quality information towards the citizen becomes an obvious and imperative need. The objective of this work was to investigate that forecasting capability using linear models (such as ARX, ARMAX, output-error and Box-Jenkins), and neural networks. They were used meteorological variables and 24-h PM10 concentration of the present day as input data. As output foreseen by the models, the 24-h PM10 concentration is obtained, with horizon of prediction of up to three days ahead. The results showed that fairly good estimates can be achieved by all of the models, but Box-Jenkins model showed best fit and predictability.
منابع مشابه
Accuracy comparison of Elamn and Jordan artificial neural networks for air particular matter concentration (PM 10) prediction using MODIS satellite images, a case study of Ahvaz.
Due to the complexity of air pollution action, artificial intelligence models specifically, neural networks are utilized to simulate air pollution. So far, numerous artificial neural network models have been used to estimate the concentration of atmospheric PMs. These models have had different accuracies that scholars are constantly exceed their efficiency using numerous parameters. The current...
متن کاملPrediction of Blasting Cost in Limestone Mines Using Gene Expression Programming Model and Artificial Neural Networks
The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone mines in Iran using the artificial neural networks (ANNs), gene expression programming (G...
متن کاملMachine learning algorithms in air quality modeling
Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...
متن کاملHourly Wind Speed Prediction using ARMA Model and Artificial Neural Networks
In this paper, a comparison study is presented on artificial intelligence and time series models in 1-hour-ahead wind speed forecasting. Three types of typical neural networks, namely adaptive linear element, multilayer perceptrons, and radial basis function, and ARMA time series model are investigated. The wind speed data used are the hourly mean wind speed data collected at Binalood site in I...
متن کاملInvestigating Financial Crisis Prediction Power using Neural Network and Non-Linear Genetic Algorithm
Bankruptcy is an event with strong impacts on management, shareholders, employees, creditors, customers and other stakeholders, so as bankruptcy challenges the country both socially and economically. Therefore, correct prediction of bankruptcy is of high importance in the financial world. This research intends to investigate financial crisis prediction power using models based on Neural Network...
متن کامل