scholarly journals Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

2020 ◽  
Vol 12 (10) ◽  
pp. 4045
Author(s):  
Muhammad Muhitur Rahman ◽  
Md Shafiullah ◽  
Syed Masiur Rahman ◽  
Abu Nasser Khondaker ◽  
Abduljamiu Amao ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Air Quality ◽  
Soft Computing ◽  
Air Quality Modeling ◽  
Atmospheric Environment ◽  
Quality Data ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Quality Modeling ◽  
Computing Models

Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.

scholarly journals Soft Computing Tool Approach for Texture Classification Using Discrete Cosine Transform

2011 ◽  
pp. 12-16
Author(s):  
Pankaj H. Chandankhede
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Learning Algorithm ◽  
Texture Classification ◽  
Fuzzy Model ◽  
Performance Comparison ◽  
Cosine Transform ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Computing Models

Texture can be considered as a repeating pattern of local variation of pixel intensities. Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing are used. A feedforward neural network is used to train the backpropagation learning algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and the remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. It is observed that the proposed neuro-fuzzy model performed better than the neural network.

Prediction of cutting tool life based on Taguchi approach with fuzzy logic and support vector regression techniques

2015 ◽  
Vol 32 (3) ◽  
pp. 270-290 ◽  
Author(s):  
Jaganathan Gokulachandran ◽  
K. Mohandas
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Tool Life ◽  
Cutting Tool ◽  
Support Vector ◽  
Taguchi Approach ◽  
Content Type ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Life Values

Purpose – The accurate assessment of tool life of any given tool is a great significance in any manufacturing industry. The purpose of this paper is to predict the life of a cutting tool, in order to help decision making of the next scheduled replacement of tool and improve productivity. Design/methodology/approach – This paper reports the use of two soft computing techniques, namely, neuro-fuzzy logic and support vector regression (SVR) techniques for the assessment of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Findings – The analysis is carried out using the two soft computing techniques. Tool life values are predicted using aforesaid techniques and these values are compared. Practical implications – The proposed approaches are relatively simple and can be implemented easily by using software like MATLAB and Weka. Originality/value – The proposed methodology compares neuro – fuzzy logic and SVR techniques.

scholarly journals Emission Inventories and Particulate Matter Air Quality Modeling over the Pearl River Delta Region

2021 ◽  
Vol 18 (8) ◽  
pp. 4155
Author(s):  
Diogo Lopes ◽  
Joana Ferreira ◽  
Ka In Hoi ◽  
Ka-Veng Yuen ◽  
Kai Meng Mok ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pearl River Delta ◽  
Air Quality Modeling ◽  
Pearl River ◽  
Emission Inventories ◽  
Atmospheric Emission ◽  
The Pearl River Delta ◽  
Quality Modeling ◽  
The Pearl River

The Pearl River Delta (PRD) region is located on the southeast coast of mainland China and it is an important economic hub. The high levels of particulate matter (PM) in the atmosphere, however, and poor visibility have become a complex environmental problem for the region. Air quality modeling systems are useful to understand the temporal and spatial distribution of air pollution, making use of atmospheric emission data as inputs. Over the years, several atmospheric emission inventories have been developed for the Asia region. The main purpose of this work is to evaluate the performance of the air quality modeling system for simulating PM concentrations over the PRD using three atmospheric emission inventories (i.e., EDGAR, REAS and MIX) during a winter and a summer period. In general, there is a tendency to underestimate PM levels, but results based on the EDGAR emission inventory show slightly better accuracy. However, improvements in the spatial and temporal disaggregation of emissions are still needed to properly represent PRD air quality. This study’s comparison of the three emission inventories’ data, as well as their PM simulating outcomes, generates recommendations for future improvements to atmospheric emission inventories and our understanding of air pollution problems in the PRD region.

Indoor air quality modeling: Compartmental approach with reactive chemistry

1982 ◽  
Vol 8 (1-6) ◽  
pp. 461-471 ◽  
Author(s):  
H. Özkaynak ◽  
P.B. Ryan ◽  
G.A. Allen ◽  
W.A. Turner
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Quality Modeling ◽  
Quality Modeling

Isoprene Emission Factors for Subtropical Street Trees for Regional Air Quality Modeling

2016 ◽  
Vol 45 (1) ◽  
pp. 234-243 ◽  
Author(s):  
Kristina A. Dunn-Johnston ◽  
Jürgen Kreuzwieser ◽  
Satoshi Hirabayashi ◽  
Lyndal Plant ◽  
Heinz Rennenberg ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Quality Modeling ◽  
Emission Factors ◽  
Street Trees ◽  
Isoprene Emission ◽  
Quality Modeling ◽  
Regional Air Quality
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