Soft Computing Techniques Applied to a Case Study of Air Quality in Industrial Areas in the Czech Republic

Arroyo, Ángel; Corchado, Emilio; Tricio, Verónica; García-Hernández, Laura; Snåšel, Václav

doi:10.1007/978-3-642-32922-7_55

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…Papadopoulos et al (2011) used MLP trained with standard back propagation algorithm for the determination of chlorinated compounds in fish. Arroyo et al (2013) applied different soft computing techniques and ANN models to analyze the atmospheric pollution conditions of two different places in Czeth Republic. García et al (2011) made a comparative study of different classical methods like MLP, support vector machines to predict the peaks of pollutant concentrations in critical meteorological situation because of particulate emission.…”

Section: Introductionmentioning

confidence: 99%

Prediction of performance and emission characteristics in a biodiesel engine using WCO ester: a comparative study of neural networks

Pai¹,

Rao²,

Vijay

2015

Soft Comput

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In this study, the applicability of Artificial Neural Networks (ANNs) has been investigated for predicting the performance and emission characteristics of a diesel engine fuelled with Waste cooking oil (WCO). ANN modeling was done using multilayer perception (MLP) and radial basis functions (RBF). In the radial basis functions, centers were initialized by two different methods namely random selection method and using clustering algorithm. In the clustering method, center initialization was done using FCM (Fuzzy c means) and CDWFCM (cluster dependent weighted fuzzy c means) algorithms. The networks were trained using the experimental data, wherein load percentage, compression ratio, blend percentage, injection timing and injection pressure were taken as the input parameters and brake thermal efficiency, brake specific energy consumption, exhaust gas temperature and engine emissions were used as the output parameters. The investigation showed that ANN predicted results matched well with the experimental results over a wide range of operating conditions for both models. A comparison was made between ANN models and regression models. ANN performed better than the regression models. Similarly a comparison of MLP and RBF indicated that RBF with CDWFCM performed better than MLP networks with lower Mean Relative Error (MRE) and higher accuracy of prediction.

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Section: Introductionmentioning

confidence: 99%

Prediction of performance and emission characteristics in a biodiesel engine using WCO ester: a comparative study of neural networks

Pai¹,

Rao²,

Vijay

2015

Soft Comput

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“…These techniques have previously been widely applied to the field of Environmental Conditions (EC) [10,11,18]. A wide range of dimensionality reduction techniques, such as Principal Component Analysis (PCA) [6], Local Linear Embedding (LLE) [25], Isometric Mapping (ISOMAP) [34] and Cooperative Maximum Likelihood Hebbian Learning (CMLHL) [13] have previously achieved very good results in the EC field [7,8]. In [7], statistical and neural models are presented for analyzing data on the emissions of atmospheric pollution in urban areas.…”

Section: Introductionmentioning

confidence: 99%

“…The main target was to classify the levels of atmospheric pollutants according to the day of the week, differentiating between working days and nonworking days. In [8], atmospheric pollution conditions at two different places in the Czech Republic were analyzed. Seven variables with atmospheric pollution information were considered and dimensionality reduction techniques (PCA, LLE, and CMLHL) were applied, to show the variability of atmospheric pollution conditions between each place, as well as the significant variability of air quality over time.…”

Section: Introductionmentioning

confidence: 99%

A hybrid intelligent system for the analysis of atmospheric pollution: a case study in two European regions

Arroyo

Herrero

Corchado

et al. 2017

Logic Journal of the IGPL

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The combined application of several soft-computing and statistical techniques is proposed for the characterization of atmospheric conditions in two European regions: Madrid (Spain) and Prague (Czech Republic). The resulting Hybrid Artificial Intelligence System (HAIS) combines projection models for dimensionality reduction and clustering, combining neural and fuzzy paradigms, in a decision support tool. In present article, this proposed HAIS is applied to analyse the air quality in these two geographical regions and get a better understanding of its circumstances and evolution. To do so, real-life data from six data-acquisition stations are analysed. The main pollutants recorded at these stations between 2007 and 2014, their geographical locations and seasonal changes are all studied, in a research that shows how such factors determine variations in air-borne pollutants. Furthermore, neural projections of the clustering results from data on atmospheric pollution are studied.

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Soft Computing Techniques Applied to a Case Study of Air Quality in Industrial Areas in the Czech Republic

Cited by 2 publications

References 17 publications

Prediction of performance and emission characteristics in a biodiesel engine using WCO ester: a comparative study of neural networks

Prediction of performance and emission characteristics in a biodiesel engine using WCO ester: a comparative study of neural networks

A hybrid intelligent system for the analysis of atmospheric pollution: a case study in two European regions

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