An Application Of Machine Learning With Boruta Feature Selection To Improve NO2 Pollution Prediction

Projecting and monitoring NO2 pollutants' concentration is perhaps an efficient and effective technique to lower people's exposure, reducing the negative impact caused by this harmful atmospheric substance. Many studies have been proposed to predict NO2 Machine learning (ML) algorithm using a diverse set of data, making the efficiency of such a model dependent on the data/feature used. This research installed and used data from 14 Internet of thing (IoT) emission sensors, combined with weather data from the UK meteorology department and traffic data from the department for transport for the corresponding time and location where the pollution sensors exist. This paper select relevant features from the united data/feature set using Boruta Algorithm. Six out of the many features were identified as valuable features in the NO2 ML model development. The identified features are Ambient humidity, Ambient pressure, Ambient temperature, Days of the week, two-wheeled vehicles(counts), cars/taxis(counts). These six features were used to develop different ML models compared with the same ML model developed using all united data/features. For most ML models implemented, there was a performance improvement when developed using the features selected with Boruta Algorithm

A Machine Learning Approach to Monitor Air Quality from Traffic and Weather data

Knowing the amount of air pollutants in our cities is of great importance to help decision makers in the definition of effective strategies aimed at maintaining a good air quality, which is a key factor for a healthy life, especially in urban environments. Using a data set from a big metropolitan city, we realize the uAQE: urban Air Quality Evaluator, which is a supervised machine learning model able to estimate air pollutants values using only weather and traffic data. We evaluate the performance of our solution by comparing the predicted pollutant values with the real measurements provided by professional air monitoring stations. We use the predicted pollutants to compute a standard Air Quality Index (AQI) and we map it into a set of five qualitative AQI classes, which can be used for decision making at the city level. uAQE is able to predict the AQI class value with an accuracy of 0.8

Arima as a forecasting tool for water quality time series measured with UV-Vis spectrometers in a constructed wetland

Hernández, N., Camargo, J., Moreno, F., Plazas-Nossa, L., & Torres, A. (September-October, 2017). Arima as a tool to predict water quality using time series recorded with UV-Vis spectrometers in a constructed wetland. Water Technology and Sciences (in Spanish), 8(5), 127-139. The prediction of water quality plays a crucial role in discussions about urban drainage systems, given that the integrated management of this resource is required in order to meet human needs. The present paper uses Arima (Autoregressive Integrated Moving Average) to predict influent and effluent water quality in a constructed wetland, as well as its pollutant removal efficiency. The wetland is located on the campus of the Pontificia Universidad Javeriana in Bogotá, Colombia. Arima prediction values were based on time series obtained with UV-Vis spectrometry probes. These predictions were found to be adequate for the first 12 hours of the water quality time series for the three data sets analyzed: influent, effluent, and efficiency. Overall, none of the data had prediction errors over 15%. In separate analyses of the relative predictive errors in influent and effluent values, they were found to be less significant for UV wavelengths than for the visible range (Vis). In addition, the variability in this type of error was less for the UV range than for the Vis range, which indicates that Arima is a suitable prediction method for analyzing pollutants that fall in the UV range

A Method for Traffic Flow Forecasting in a Large-Scale Road Network Using Multifeatures

Accurate traffic prediction on a large-scale road network is significant for traffic operations and management. In this study, we propose an equation for achieving a comprehensive and accurate prediction that effectively combines traffic data and non-traffic data. Based on that, we developed a novel prediction model, called the adaptive deep neural network (ADNN). In the ADNN, we use two long short-term memory (LSTM) networks to extract spatial-temporal characteristics and temporal characteristics, respectively. A backpropagation neural network (BPNN) is also employed to represent situations from contextual factors such as station index, forecast horizon, and weather. The experimental results show that the prediction of ADNN for different stations and different forecast horizons has high accuracy; even for one hour ahead, its performance is also satisfactory. The comparison of ADNN and several benchmark prediction models also indicates the robustness of the ADNN

A városklíma jellegzetességei és hatásai

A harmadik évezred elején a városok robbanásszerű növekedésének eredményeként a Föld népességének közel fele – hozzávetőleg 3 milliárd fő – él városokban. Az urbani-záció gyorsulása a vele járó környezeti problémák felerősödését is kiváltotta. A városi lakosság gyarapodásával egyre nagyobb számú népességet érintenek közvetlenül a kedvezőtlen környezeti hatások. Közöttük fontos helyet foglalnak el a meteorológiai, éghajlati következmények. Alapvető jelentőségű a felszín fizikai jellemzőinek és a le-vegő összetételének megváltozása a beépített területeken, amelyek legszembetűnőbb hatása a levegőminőség romlása, ezenkívül azonban szinte az összes meteorológiai elem megváltozik kisebb-nagyobb mértékben a külterülethez képest. A városklíma ki-fejezés összefoglalóan azt fejezi ki, hogy a települések beépített területén sajátos helyi klíma, azaz a város környéki területekétől eltérő éghajlat jön létre. A városklíma jelen-ségei közül e munka keretében az egyik fontos éghajlat-módosulással, a városok terü-letén a környező beépítetlen felszínekhez viszonyítva kialakuló hőmérsékleti többlet, az úgynevezett városi hősziget (urban heat island – UHI) vizsgálatával foglalkozunk