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 Development of Land Use Regression Model for Seasonal Variation of Nitrogen Dioxide (NO2) in Lahore, Pakistan

2021 ◽  
Vol 13 (9) ◽  
pp. 4933
Author(s):  
Saimar Pervez ◽  
Ryuta Maruyama ◽  
Ayesha Riaz ◽  
Satoshi Nakai
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Urban Areas ◽  
Industrial Development ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Land Use Classification ◽  
Land Use Regression ◽  
Air Exposure

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

scholarly journals An artificial neural network approach integrating plasma proteomics and genetic data identifies PLXNA4 as a new susceptibility locus for pulmonary embolism

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Misbah Razzaq ◽  
Maria Jesus Iglesias ◽  
Manal Ibrahim-Kosta ◽  
Louisa Goumidi ◽  
Omar Soukarieh ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Genome Wide Association Study ◽  
Susceptibility Gene ◽  
Biological Traits ◽  
Nucleotide Polymorphisms ◽  
Ann Model ◽  
Neural Network Approach ◽  
A Genome ◽  
Increased Risk ◽  
Artificial Neural

AbstractVenous thromboembolism is the third common cardiovascular disease and is composed of two entities, deep vein thrombosis (DVT) and its potential fatal form, pulmonary embolism (PE). While PE is observed in ~ 40% of patients with documented DVT, there is limited biomarkers that can help identifying patients at high PE risk. To fill this need, we implemented a two hidden-layers artificial neural networks (ANN) on 376 antibodies and 19 biological traits measured in the plasma of 1388 DVT patients, with or without PE, of the MARTHA study. We used the LIME algorithm to obtain a linear approximate of the resulting ANN prediction model. As MARTHA patients were typed for genotyping DNA arrays, a genome wide association study (GWAS) was conducted on the LIME estimate. Detected single nucleotide polymorphisms (SNPs) were tested for association with PE risk in MARTHA. Main findings were replicated in the EOVT study composed of 143 PE patients and 196 DVT only patients. The derived ANN model for PE achieved an accuracy of 0.89 and 0.79 in our training and testing sets, respectively. A GWAS on the LIME approximate identified a strong statistical association peak (rs1424597: p = 5.3 × 10–7) at the PLXNA4 locus. Homozygote carriers for the rs1424597-A allele were then more frequently observed in PE than in DVT patients from the MARTHA (2% vs. 0.4%, p = 0.005) and the EOVT (3% vs. 0%, p = 0.013) studies. In a sample of 112 COVID-19 patients known to have endotheliopathy leading to acute lung injury and an increased risk of PE, decreased PLXNA4 levels were associated (p = 0.025) with worsened respiratory function. Using an original integrated proteomics and genetics strategy, we identified PLXNA4 as a new susceptibility gene for PE whose exact role now needs to be further elucidated.

Artificial Neural Networks and Their Applications in Business

2018 ◽  
pp. 6642-6657
Author(s):  
Trevor J. Bihl ◽  
William A. Young II ◽  
Gary R. Weckman
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Model Complexity ◽  
Ann Model ◽  
Processing Methods ◽  
Starting Point ◽  
Clustering And Classification ◽  
Artificial Neural

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in Big Data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs is then discussed. Common pre- and post- data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business related endeavors for further reading.

Artificial Neural Networks and Their Applications in Business

2019 ◽  
pp. 1009-1027
Author(s):  
Trevor J. Bihl ◽  
William A. Young II ◽  
Gary R. Weckman
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Model Complexity ◽  
Ann Model ◽  
Processing Methods ◽  
Starting Point ◽  
Clustering And Classification ◽  
Artificial Neural

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in big data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering, and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) to highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs are then discussed. Common pre- and post-data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business-related endeavors for further reading.

scholarly journals Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach

2020 ◽  
Vol 48 (1) ◽  
pp. 366-377 ◽  
Author(s):  
Yeşim Benal ÖZTEKİN ◽  
Alper TANER ◽  
Hüseyin DURAN
Keyword(s):  
Neural Networks ◽  
Transfer Functions ◽  
Geometric Mean ◽  
Back Propagation ◽  
Castanea Sativa ◽  
Average Error ◽  
Ann Model ◽  
Neural Network Approach ◽  
Castanea Sativa Mill ◽  
Artificial Neural

The present study investigated the possible use of artificial neural networks (ANN) to classify five chestnut (Castanea sativa Mill.) varieties. For chestnut classification, back-propagation neural networks were framed on the basis of physical and mechanical parameters. Seven physical and mechanical characteristics (geometric mean diameter, sphericity, volume of nut, surface area, shell thickness, shearing force and strength) of chestnut were determined. It was found that these characteristics were statistically different and could be used in the classification of species. In the developed ANN model, the design of the network is 7-(5-6)-1 and it consists of 7 input, 2 hidden and 1 output layers. Tansig transfer functions were used in both hidden layers, while linear transfer functions were used in the output layer. In ANN model, R2 value was obtained as 0.99999 and RMSE value was obtained as 0.000083 for training. For testing, R2 value was found as 0.99999 and RMSE value was found as 0.00031. In the approximation of values obtained with ANN model to the values measured, average error was found as 0.011%. It was found that the results found with ANN model were very compatible with the measured data. It was found that the ANN model obtained can classify chestnut varieties in a fast and reliable way.

scholarly journals Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

2020 ◽  
Vol 17 (6) ◽  
pp. 2099 ◽  
Author(s):  
Martin Otto Paul Ramacher ◽  
Matthias Karl
Keyword(s):  
Air Pollution ◽  
Population Dynamics ◽  
Urban Areas ◽  
Road Traffic ◽  
Ambient Air ◽  
Traffic Emissions ◽  
Exposure Model ◽  
Population Exposure ◽  
Road Traffic Emissions ◽  
Pollutant Concentrations

To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen’s health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO2 and 19% more to PM2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO2 and PM2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO2 and 15% for PM2.5). Results for population-weighted exposure revealed exposure to PM2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from −12% to +7% for NO2 and PM2.5. The developed “dynamic transport approach” is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

scholarly journals Ambient air quality in an urban area and its effects on plants and human beings: a case study of Tiruchirappalli, India

1970 ◽  
Vol 6 (2) ◽  
pp. 13-19 ◽  
Author(s):  
Sirajuddin M Horaginamani ◽  
M Ravichandran
Keyword(s):  
Air Pollution ◽  
Developing Countries ◽  
Air Quality ◽  
Human Health ◽  
Urban Area ◽  
Urban Areas ◽  
Ambient Air ◽  
Control Measures ◽  
Human Beings ◽  
Ambient Air Quality

Though water and land pollution is very dangerous, air pollution has its own peculiarities, due to its transboundary dispersion of pollutants over the entire world. In any well planned urban set up, industrial pollution takes a back seat and vehicular emissions take precedence as the major cause of urban air pollution. Air pollution is one of the serious problems faced by the people globally, especially in urban areas of developing countries like India. All these in turn lead to an increase in the air pollution levels and have adverse effects on the health of people and plants. Western countries have conducted several studies in this area, but there are only a few studies in developing countries like India. A study on ambient air quality in Tiruchirappalli urban area and its possible effects selected plants and human health has been undertaken, which may be helpful to bring out possible control measures. Keywords: ambient air quality; respiratory disorders; APTI; human health DOI: 10.3126/kuset.v6i2.4007Kathmandu University Journal of Science, Engineering and Technology Vol.6. No II, November, 2010, pp.13-19

Numerical simulation of air pollution in urban areas: Model development

1987 ◽  
Vol 21 (1) ◽  
pp. 201-212 ◽  
Author(s):  
Jia-Yeong Ku ◽  
S.Trivikrama Rao ◽  
K.Shankar Rao
Keyword(s):  
Numerical Simulation ◽  
Air Pollution ◽  
Urban Areas ◽  
Model Development

scholarly journals Modeling of methane emissions using artificial neural network approach

2015 ◽  
Vol 80 (3) ◽  
pp. 421-433 ◽  
Author(s):  
Lidija Stamenkovic ◽  
Davor Antanasijevic ◽  
Mirjana Ristic ◽  
Aleksandra Peric-Grujic ◽  
Viktor Pocajt
Keyword(s):  
Neural Network ◽  
National Level ◽  
Model Performance ◽  
General Regression Neural Network ◽  
Annual Data ◽  
Ann Model ◽  
Neural Network Approach ◽  
Ch4 Emissions ◽  
Artificial Neural ◽  
Mlr Model

The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN) with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN) and a General Regression Neural Network (GRNN). A conventional multiple linear regression (MLR) model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies.

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