Artificial Neural Networks in Vehicular Pollution Modelling

Khare, Mukesh; Nagendra, S.M. Shiva

doi:10.1007/978-3-540-37418-3

Cited by 38 publications

(25 citation statements)

References 168 publications

(296 reference statements)

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“…Activation or transfer functions for an ANNs hidden neurons are required to introduce non-linearity into the network. In the absence of non-linearity, the hidden neurons would not make the ANN more powerful (Khare and Shiva Nagendra, 2007). The selection of the appropriate activation or transfer function substantially influences the ANNs performance (Sharma et al, 2015).…”

Section: Artificial Neural Network Transfer Functionmentioning

confidence: 99%

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

2018

IJTTE

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Abstract:In this paper an Artificial Neural Network (ANN) is proposed for predicting Australia's annual export air cargo demand. The modelling in the study was based on annual data from 1993 to 2016. The ANN model was developed using the input parameters of world real merchandise exports, world population growth, world jet fuel prices, world air cargo yields (proxy for air cargo costs), outbound flights from Australia, and Australian/ United States dollar exchange rate and two dummy variables, which controlled for the strong cyclical fluctuations in air cargo demand which occurred in 2003 and 2015. The artificial neural network (ANN) used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The ANN was applied during training, testing and validation and had 8 inputs, 1 neuron in the hidden layer and 1 neuron in the output layer. The data was randomly divided into three data sets; training, testing and model validation. The best-fit model was selected according to four goodness-of-fit measures: mean absolute error (MAE), mean square error (MSE), root mean square errors (RMSE), and mean absolute percentage errors (MAPE). The highest R-value obtained from the ANN model is 0.97844. The results suggest that the ANN model is an efficient tool for predicting Australia's annual export air cargo demand.

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Section: Artificial Neural Network Transfer Functionmentioning

confidence: 99%

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

2018

IJTTE

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“…hydrocarbons, nitrogen and sulphur dioxides (Goyal et al 2006;Khare and Nagendra 2007;Dursun et al 2012). The number of factories in Konya is over one thousand, and number of employees are 100,000 in the industrial sector (Güçlü and Dursun 2010).…”

Section: Introductionmentioning

confidence: 99%

Modelling sulphur dioxide levels of Konya city using artificial intelligent related to ozone, nitrogen dioxide and meteorological factors

Dursun

Kunt

Taylan

2015

Int. J. Environ. Sci. Technol.

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Increasing industrial developments increased the environmental pollution problems in many cities of the world. Air quality modelling and indexes are used to introduce the information on local air quality indicators in polluted regions. Estimation and monitoring of air quality in the city centres are important due to environmental health and comfort of human-related topics. Air quality approximation is a complicate subject that artificial intelligent techniques are successfully used for modelling the complicated and nonlinear approximation problems. In present study, artificial neural network and an adaptive neuro-fuzzy logic method developed to approximate the impact of certain environmental conditions on air quality and sulphur dioxide pollution level and used with this study in Konya city centre. Data of sulphur dioxide concentrations were collected from 15 selected points of Konya city for prediction of air quality. Using air quality standards, air quality was discussed by considering the sulphur dioxide concentration as independent variables with meteorological parameters. Different meteorological parameters were used for investigation of pollution relation. One of the important modelling tools, adaptive network-based fuzzy inference system model, was used to assess performance by a number of checking data collected from different sampling stations in Konya. The outcomes of adaptive network-based fuzzy inference system model was evaluated by fuzzy quality charts and compared to the results obtained from Turkey and Environmental Protection Agency air quality standards. From the present results, fuzzy rule-based adaptive network-based fuzzy inference system model is a valuable tool prediction and assessment of air quality and tends to propagate accurate results.

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“…The computational capabilities of ANNs have been already utilized to estimate exhaust concentrations as a function of traffic and meteorological variables in [36]. Unlike the computational electromagnetic (CEM) tools, ANNs can provide time varying inputs and deal with the problem as a black box.…”

Section: Introductionmentioning

confidence: 99%

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

Wefky¹,

Espinosa²,

Leferink³

et al. 2013

PIER

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Abstract-This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using Renault Twizy. Time-domain electromagnetic interference (TDEMI) measurement techniques have been employed to record the radiated disturbances in the 150 kHz-30 MHz range. Interesting emissions have been found in the range 150 kHz-3.8 MHz approximately. The instantaneous vehicle speed and acceleration have been chosen to represent the vehicle operational modes. A comparative study of the prediction performance between different static and dynamic neural networks has been done. Results showed that a Multilayer Perceptron (MLP) model trained with extreme learning machines (ELM) has achieved the best prediction results. The proposed model has been used to estimate the radiated magnetic field levels of an urban trip carried out with a Think City electric car.

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Artificial Neural Networks in Vehicular Pollution Modelling

Cited by 38 publications

References 168 publications

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

Modelling sulphur dioxide levels of Konya city using artificial intelligent related to ozone, nitrogen dioxide and meteorological factors

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

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