Missing values reconstruction in sound level monitoring station by means of intelligent computing

Abstract

The aim of the paper was to reconstruct the missing data by applying the model which describes variability of sound level in the whole period from 2013 to 2016. To build the model, the computational intelligence methods, like fuzzy systems, or regression trees can be used. The latter approach was applied and we built the model with Cubist regression tree software, using equivalent sound levels recorded in 2013. For the reconstruction of sound level data in short period of time (several days), time series values and day_of_week values together should be used in the training dataset. For the reconstruction of sound level data in long period of time (several months) day_of_week values should be used in the training dataset.

Application of selected computational intelligence methods to sound level modelling based on traffic intensity in thoroughfare

“…For these reasons author decided (in Section 3) to propose the new model, inspired by Nordic Prediction Method. Other studies showed that regression as well as artificial neural networks can be used for modelling as a function of traffic intensity parameters [7], or even without direct use of vehicle volume and speed [8,9].…”

“…calculated from measurements for day sub-intervals(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) in year 2013The distribution of values from the above mentioned 296 complete records is shown inFig. 2.…”