scholarly journals Urban Ozone Concentration Forecasting with Artificial Neural Network in Corsica

2014 ◽  
Vol 10 (1) ◽  
pp. 29-37
Author(s):  
Wani Tamas ◽  
Gilles Notton ◽  
Christophe Paoli ◽  
Cyril Voyant ◽  
Marie-Laure Nivet ◽  
...  
Keyword(s):  
Air Quality ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Atmospheric Pollutants ◽  
Saharan Dust ◽  
Ann Model ◽  
Deterministic Models ◽  
Artificial Neural ◽  
Dust Events ◽  
Short Term Prediction

Abstract Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France), needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, a good knowledge of atmospheric processes and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs) that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events). Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88.

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

scholarly journals Effect of Atmospheric Pollutants on the Air Quality in Tunisia

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Karim Bouchlaghem ◽  
Blaise Nsom
Keyword(s):  
Air Quality ◽  
Atmospheric Pollutants ◽  
Saharan Dust ◽  
Aerodynamic Diameter ◽  
Concentration Data ◽  
Air Masses ◽  
African Dust ◽  
Yearly Average ◽  
Daily Concentration

This paper presents the evolution of Saharan dust advection when the PM10 (particles with an aerodynamic diameter below 10 μm) concentration exceeds standard limits in different Tunisian sites. Meteorological and concentration data (from 2004 to 2010) obtained from several monitoring stations and in situ measurements were used to identify African dust change in seasonal occurrence, their source origin, and their impact on surface PM10 concentrations. We pointed out that the Saharan dust contribution caused frequently the surpassing of the maximum number of days in excess of EU standard limits as well as of the maximum yearly average in the Mediterranean Tunisian coasts. The maximum daily concentration reaches 439 μg/m3during the Saharan events. The decrease in particulate levels recorded at the end of each event is due to the injection of European air masses and rainfalls. Primary pollutants peaks were much higher in winter than in summer which can be explained on the basis of the lower ventilation and mixing.

scholarly journals Experimental and artificial neural network approach for forecasting of traffic air pollution in urban areas: The case of Subotica

2010 ◽  
Vol 14 (suppl.) ◽  
pp. 79-87 ◽  
Author(s):  
Bogdana Vujic ◽  
Srdjan Vukmirovic ◽  
Goran Vujic ◽  
Nebojsa Jovicic ◽  
Gordana Jovicic ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Meteorological Data ◽  
Model Development ◽  
Ambient Air ◽  
Ann Model ◽  
Statistical Parameters ◽  
Neural Network Approach ◽  
Starting Point ◽  
Artificial Neural

In the recent years, artificial neural networks (ANNs) have been used to predict the concentrations of various gaseous pollutants in ambient air, mainly to forecast mean daily particle concentrations. The data on traffic air pollution, irrespective of whether they are obtained by measuring or modelling, represent an important starting point for planning effective measures to improve air quality in urban areas. The aim of this study was to develop a mathematical model for predicting daily concentrations of air pollution caused by the traffic in urban areas. For the model development, experimental data have been collected for 10 months, covering all four seasons. The data about hourly concentration levels of suspended particles with aerodynamic diameter less than 10 ?m (PM10) and meteorological data (temperature, air humidity, speed and direction of wind), measured at the measuring station in the town of Subotica from June 2008 to March 2009, served as the basis for developing an ANN-based model for forecasting mean daily concentrations of PM10. The quality of the ANN model was assessed on the basis of the statistical parameters, such as RMSE, MAE, MAPE, and r.

scholarly journals Analysis of Crowd Flow Parameters Using Artificial Neural Network

2018 ◽  
Vol 19 (4) ◽  
pp. 335-345
Author(s):  
Poojari Yugendar ◽  
K.V.R. Ravishankar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
The Other ◽  
Ann Model ◽  
Deterministic Models ◽  
Accuracy Measurement ◽  
Flow Parameters ◽  
Non Linear ◽  
Artificial Neural

Abstract Research scientists have been developing mathematical tools to detect objects, recognize objects and actions, and discover behaviours and events to human abilities. In all these efforts, the understanding of human actions is of a special interest for both application and research purposes. In this study, crowd flow parameters are analysed by considering linear and non linear relationships between stream flow parameters using conventional and soft computing approach. Deterministics models like Greenshield and Underwood were applied in the study to describe flow characteristics. A non-linear model based on Artificial Neural Network (ANN) approach is also used to build a relationship between different crowd flow parameters and compared with the other deterministic models. ANN model gave good results based on accuracy measurement to deterministic models because of their self-processing and intelligent behaviour. Mean absolute error (MAE) and root mean square error (RMSE) values for the best fitted ANN model are less than those for the other models. ANN model gives better performance in fitness of model and future prediction of flow parameters.

scholarly journals Comparison of Application of AERMOD and ISCST3 Models for Simulating the Dispersion of Emitted Pollutant from the Stack of an Industrial Plant in Different Time Scales

2019 ◽  
Author(s):  
Mehrshad Bajoghli
Keyword(s):  
Air Quality ◽  
Meteorological Data ◽  
Ground Level ◽  
Atmospheric Pollutants ◽  
Industrial Plant ◽  
Simultaneous Application ◽  
Modeling Process ◽  
Total Suspended Particles ◽  
Decision Methods ◽  
Simulated Concentration

Background: One of the main parts of air quality management is known as modeling of atmospheric pollutants. In this regards, simultaneous application of several models in a project and comparing the results obtained from these models could have been a considerable contribution to air quality managers for taking a more efficient decision. Methods: In this study, the stack of an industrial plant in the southwest of Isfahan was selected as the emission source and the total suspended particles emitted from this stack was simulated by applying AERMOD and ISCST3 view models (version 8.2). In this vein, the modeling process was conducted using MM5 meteorological data in a 50 50 km extent with 2000 m network distance for each of the models in 1-h, 24-h term averages (short term averages) and monthly and annual periods (long term averages) at ground level concentrations (GLC). Results: Results indicated that the highest simulated concentration for both models occurred in a 2000 meters’ distance in the east of the stack. Moreover, the highest simulated concentration applying AERMOD was lower than that of applying ISCST3 in all term averages which is due to existing differences between applied algorithms in these two models. Conclusion: Consequently, applying AERMOD due to the use of more advanced and up-to-date algorithms have priority over ISCST3 model. Applying ISCST3 can also be useful for small projects that require less input data compared to the AERMOD.

scholarly journals Modelling the effects of meteorological parameters on water temperature using artificial neural networks

2018 ◽  
Vol 77 (6) ◽  
pp. 1724-1733 ◽  
Author(s):  
Merve Temizyurek ◽  
Filiz Dadaser-Celik
Keyword(s):  
Water Temperature ◽  
Meteorological Parameters ◽  
Meteorological Data ◽  
Activation Function ◽  
Quality Parameter ◽  
Gradient Algorithm ◽  
Water Quality Parameter ◽  
Ann Model ◽  
Water Temperature Data ◽  
Artificial Neural

Abstract Water temperature affects all biological and chemical processes in water; therefore, it is an extremely important water quality parameter. Meteorological factors are among the most important factors that affect water temperatures. The aim of this study is to develop an artificial neural network (ANN) model to investigate the effects of meteorological parameters on water temperatures at Kızılırmak River in Turkey. Water temperature data were collected from gauging stations on Kızılırmak River, and meteorological data were acquired from the nearest meteorological stations. Air temperature, wind speed, relative humidity, and previous water temperatures were formed the input parameters. The model output included water temperatures. All data were available for the 1995–2007 period, with occasional missing records. The activation functions of the ANN model and the number of neurons in the hidden layer were selected by trial-and-error method to find the best results. The root mean square error and the correlation coefficient between observed and simulated water temperatures were used to assess the model success. The best results were obtained by using sigmoid activation function and scaled conjugate gradient algorithm. This study showed that meteorological data can be used to simulate water temperature with ANN model for Kızılırmak River.

scholarly journals Wireless Sensors Network Monitoring of Saharan Dust Events in Bari, Italy

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 898 ◽  
Author(s):  
Michele Penza ◽  
Domenico Suriano ◽  
Valerio Pfister ◽  
Mario Prato ◽  
Gennaro Cassano
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Cost Effective ◽  
Saharan Dust ◽  
Quality Monitoring ◽  
Sensor Data ◽  
Air Quality Monitoring ◽  
Dust Events ◽  
Air Quality Monitoring Stations ◽  
Monitoring Stations

A sensors network based on 8 stationary nodes distributed in Bari (Southern Italy) hasbeen deployed for urban air quality monitoring during advection events of Saharan dust in theperiod 2015–2017. The low-cost sensor-systems have been installed in specific sites (buildings,offices, schools, streets, airport) to assess the PM10 concentration at high spatial and temporalresolution in order to supplement the expensive official air monitoring stations for citizen sciencepurposes. Continuous measurements were performed by a cost-effective optical particle counter(PM10), including temperature and relative humidity sensors. They are operated to assess theperformance during a long-term campaign (July 2015–December 2017) of 30 months for smart citiesapplications. The sensor data quality has been evaluated by comparison to the reference data of the9 Air Quality Monitoring Stations (AQMS), managed by local environmental agency (ARPA-Puglia)in the Bari city.

scholarly journals An Outlook of Ozone Air Pollution through Comparative Analysis of Artificial Neural Network, Regression, and Sensitivity Models

2018 ◽  
Vol 8 (5) ◽  
pp. 3387-3391
Author(s):  
J. S. Khan ◽  
S. Khoso ◽  
Z. Iqbal ◽  
S. Sohu ◽  
M. A. Keerio
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Artificial Neural Network ◽  
Ozone Concentration ◽  
Government Agencies ◽  
Ann Model ◽  
Sustainable Environment ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Climate Prediction Center

Air pollution and atmospheric ozone can cause damages to human health and to the environment. This study explores the potential approach of the artificial neural network (ANN) model and compares it with a regression model for predicting ozone concentration using different parameters and functions measured by the Climate Prediction Center of US National Weather Service. In addition, this study has compared the economic viability of ANN and other measuring methods. Results showed that the ANN-based model exhibited better performance. Such model types can be beneficial to government agencies. By predicting ozone concentration government agencies can take preventive measures to avoid significant health effects, protect local populations, and help preserve a sustainable environment.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

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