Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine

Machine learning-based Decision Tree J48 with grey wolf optimizer for environmental pollution control

ABSTRACTDue to industrialization, activities of human and urbanization, environment is getting polluted. Air pollution has become a main issue in the metropolitan areas of the world. To protect people from diseases, monitoring air quality plays an important thing. This air pollutant may lead to many health issues like respiratory and cardiac problems. The major air pollutants are NO, C₆H₆, CO, etc. Many research works have been done in predicting air pollution-based health issues, predicting air pollution levels, monitoring and controlling the polluted levels. But they are not efficient, cost of maintenance is high and insufficient tool for monitoring it. To overcome these issues, this paper implements hybrid algorithm of Decision Tree J48 and Grey Wolf Optimizer (DT-GWO). This DT-GWO is a better model to addresses the predicting of Air Quality Index (AQI), which minimizes the error rate, accurately and effectively predicting the air quality. The AQI values are categorised as good, moderate, unhealthy, very unhealthy and hazardous. The dataset used in this work is collected from Kaggle website which contains air pollutants details with air quality index values. Accuracy obtained for decision Tree J48 is 93.72%, grey wolf optimizer is 96.83% and our proposed work DT-GWO is 99.78%.

Environmental gaseous pollutants are related to increase of acute coronary syndrome in Valencia region

INTRODUCTION AND AIMS

Environmental pollution are one of the most relevant risk factors to atherosclerosis. To know awareness about the importance of urban air pollution as a trigger for hospital admission due to acute coronary syndrome (ACS), this study analyzed levels of different gaseous air pollutants in the air and its correlation with number of ACS.

METHODS

Epidemiological data of patients admitted for ACS in five towns during the years 2006-2008 were recovered. Clinical data regarding admissions for ACS were obtained from the hospital admission services. Measures of seven air contaminants were recovered from the environmental stations. Mixed model including sex, age, location, and the average levels of air pollutants contaminants as fixed effects and its interaction were performed.

RESULTS

The incidence rate of ACS is higher in man than woman, and higher in older people than young. The maximum ACS were in the last trimester of the year, was the most elevated levels of gaseous pollutants have been found. Levels of NO₂, NO, and CO are positively correlated between them, and negatively correlated with O₃ levels. All air pollutants analyzed increase the number of ACS hospital admission in the five locations evaluated.

CONCLUSIONS

Levels of gaseous pollutants are related between them, being the levels of NO₂, NO, and CO, positively correlated, and negatively correlated with levels of O₃. Number of ACS hospital admission increases with levels of five air gaseous pollutants studied.

Application of a Machine Learning Method for Prediction of Urban Neighborhood-Scale Air Pollution

Urban air pollution has aroused growing attention due to its associated adverse health effects. A model which could promptly predict urban air quality with considerable accuracy is, therefore, important and will benefit the development of smart cities. However, only a computational fluid dynamics (CFD) model could better resolve the dispersion behavior within an urban canyon layer. A machine learning (ML) model using the Artificial Neural Network (ANN) approach was formulated in the current study to investigate vehicle-derived airborne particulate (PM₁₀) dispersion within a compact high-rise-built environment. Various measured meteorological parameters and PM₁₀ concentrations were adopted as the model inputs to train the ANN model. A building-resolved CFD model under the same environmental settings was also set up to compare its model performance with the ANN model. Our results showed that the ANN model exhibited promising performance (r = 0.82, fractional bias = 0.002) when comparing the > 1000 h PM₁₀ measurements. When comparing the diurnal hourly measured PM₁₀ variations in a clear-sky day, both the ANN and CFD models performed well (r > 0.8). The good performance of the CFD model relied on the knowledge of the in situ diurnal traffic profile, the adoption of suitable mobile source emission factor(s) (e.g., from MOBILE 6 and COPERT4), and the use of urban thermal and dynamical variables to capture PM₁₀ variations in both neutral and unstable atmospheric conditions. These requirements/constraints make it impractical for daily operation. On the contrary, the ML (ANN) model adopted here is free from these constraints and is fast (less than 0.1% computational time relative to the CFD model). These results demonstrate that the ANN model is a superior option for a smart city application.

Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey

With the increase in population and industrialization, air pollution has become one of the global problems nowadays. Therefore, air pollutant parameters should be measured at regular intervals, and the necessary measures should be taken by evaluating the results of measurements. In order to prevent air pollution, pollutant parameters must be evaluated within the framework of a model. Recently, in order to obtain objective and more sensitive results with regard to air pollution nowadays, studies, which use machine learning algorithms in artificial intelligence technologies, have been carried out. In this study, PM₁₀ concentrations, which are obtained from 7 stations in Ankara province in Turkey, were trained with machine learning algorithms (LASSO, SVR, RF, kNN, xGBoost, ANN). The PM₁₀ concentrations of the years 2009-2017 of 6 stations in Ankara were given as input, and the PM₁₀ concentrations of the seventh station for the year 2018 were predicted. The model development stage was repeated for each station, and the performance and error rates of the algorithms were determined by comparing the results produced by the algorithms with the actual results. The best results were provided with ANN (R² = 0.58, RMSE = 20.8, MAE = 14.4). The spatial distribution of the estimated concentration results was provided through Geographic Information System (GIS), and spatial strategies for improving air pollution over land use were established.

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

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.

Development and applications of an intelligent crow search algorithm based on opposition based learning

Metaheuristics are proven beneficial tools for solving complex, hard optimization problems. Recently, a plethora of work has been reported on bio inspired optimization algorithms. These algorithms are mimicry of behavior of animals, plants and processes into mathematical paradigms. With these developments, a new entrant in this group is Crow Search Algorithm (CSA). CSA is based on the strategic behavior of crows while searching food, thievery and chasing behavior. This algorithm sometimes suffers with local minima stagnation and unbalance exploration and exploitation phases. To overcome this problem, a cosine function is proposed first, to accelerate the exploration and retard the exploitation process with due course of the iterative process. Secondly the opposition based learning concept is incorporated for enhancing the exploration virtue of CSA. The evolved variant with the inculcation of these two concepts is named as Intelligent Crow Search Algorithm (ICSA). The algorithm is benchmarked on two benchmark function sets, one is the set of 23 standard test functions and another is set of latest benchmark function CEC-2017. Further, the applicability of this variant is tested over structural design problem, frequency wave synthesis problem and Model Order Reduction (MOR). Results reveal that ICSA exhibits competitive performance on benchmarks and real applications when compared with some contemporary optimizers.

Time-Shift Multi-scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing

Multi-scale permutation entropy (MPE) is an effective nonlinear dynamic approach for complexity measurement of time series and it has been widely applied to fault feature representation of rolling bearing. However, the coarse-grained time series in MPE becomes shorter and shorter with the increase of the scale factor, which causes an imprecise estimation of permutation entropy. In addition, the different amplitudes of the same patterns are not considered by the permutation entropy used in MPE. To solve these issues, the time-shift multi-scale weighted permutation entropy (TSMWPE) approach is proposed in this paper. The inadequate process of coarse-grained time series in MPE was optimized by using a time shift time series and the process of probability calculation that cannot fully consider the symbol mode is solved by introducing a weighting operation. The parameter selections of TSMWPE were studied by analyzing two different noise signals. The stability and robustness were also studied by comparing TSMWPE with TSMPE and MPE. Based on the advantages of TSMWPE, an intelligent fault diagnosis method for rolling bearing is proposed by combining it with gray wolf optimized support vector machine for fault classification. The proposed fault diagnostic method was applied to two cases of experimental data analysis of rolling bearing and the results show that it can diagnose the fault category and severity of rolling bearing accurately and the corresponding recognition rate is higher than the rate provided by the existing comparison methods.

Image Processing-Based Recognition of Wall Defects Using Machine Learning Approaches and Steerable Filters

Detection of defects including cracks and spalls on wall surface in high-rise buildings is a crucial task of buildings' maintenance. If left undetected and untreated, these defects can significantly affect the structural integrity and the aesthetic aspect of buildings. Timely and cost-effective methods of building condition survey are of practicing need for the building owners and maintenance agencies to replace the time- and labor-consuming approach of manual survey. This study constructs an image processing approach for periodically evaluating the condition of wall structures. Image processing algorithms of steerable filters and projection integrals are employed to extract useful features from digital images. The newly developed model relies on the Support vector machine and least squares support vector machine to generalize the classification boundaries that categorize conditions of wall into five labels: longitudinal crack, transverse crack, diagonal crack, spall damage, and intact wall. A data set consisting of 500 image samples has been collected to train and test the machine learning based classifiers. Experimental results point out that the proposed model that combines the image processing and machine learning algorithms can achieve a good classification performance with a classification accuracy rate = 85.33%. Therefore, the newly developed method can be a promising alternative to assist maintenance agencies in periodic building surveys.