Ground-level O3 sensitivity analysis using support vector machine with radial basis function
01 Jun 2019-International Journal of Environmental Science and Technology (Springer Berlin Heidelberg)-Vol. 16, Iss: 6, pp 2745-2754
TL;DR: Kernel trick as a crucial function for support vector machines is analyzed in the current study, and the radial basis function is illustrated as the best kernel trick for this study in comparison with polynomial, linear, multilayer perceptron tricks.
Abstract: Previous research studies have revealed human susceptibility to tropospheric ozone and consequently huge amount of investments allocating to monitor and research about this pollutant. High expenditures of monitoring the air contaminants and needing for spacious facilities can be decreased by applying the soft computing approaches and new technologies. In this paper, support vector machine, a powerful approach with reliable results in previous studies, is applied to predict the tropospheric ozone for Tehran, Iran, metropolitan area. Four photochemical precursors and three meteorological parameters are assumed as predictors. The impact of all parameters is scrutinized, and the best model of input combinations is depicted: RMSE = 0.0774 and R = 0.8459. Trying to find out each parameter impact, the datasets are divided into different groups and are used as input combinations where the outcomes depicted that particulate matters (PM2.5 and PM10), ambient air temperature (T), CO and NO2 are the most effective parameters on the O3 value tolerances. Kernel trick as a crucial function for support vector machines is analyzed in the current study, and the radial basis function is illustrated as the best kernel trick for this study in comparison with polynomial, linear, multilayer perceptron tricks. Finally, to calibrate the measuring instruments, using the support vector machine with radial basis function can represent an acceptable result for the best input combination.
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TL;DR: In this article, a method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations is presented.
Abstract: Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations.
The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.
73 citations
TL;DR: Wang et al. as discussed by the authors proposed a hybrid sequence-to-sequence model embedded with the attention mechanism for predicting regional ground-level ozone concentration, where the inherent spatiotemporal correlations in air quality monitoring network are simultaneously extracted, learned and incorporated, and auxiliary air pollution and meteorological information are adaptively involved.
62 citations
TL;DR: In this article, three statistical modeling methods: (i) decision tree (DT), (ii) Bayesian network (BN), and (iii) support vector machine (SVM) were used to develop the models.
Abstract: Particulate matter has major impacts on human health in urban regions, and Tehran is one of the most polluted metropolitan cities in the world, struggling to control this pollutant more than any other contaminant. PM2.5 concentrations were predicted by three statistical modeling methods: (i) decision tree (DT), (ii) Bayesian network (BN), and (iii) support vector machine (SVM). Collected data for three consecutive years (January 2013 to January 2016) were used to develop the models. Data from the initial 2 years were employed as the training data, and measurements from the last year were used for testing the models. Twelve parameters, covering meteorological variables and concentrations of several chemical species, were explored as potential predictors of PM2.5. According to the sensitivity analysis of PM2.5 by SVM and derived explicit equations from BN and DT, PM10, NO2, SO2, and O3 are the most important predictors. Furthermore, the impacts of the predictors on the PM2.5 were assessed which the chemical precursors’ influences indicated more in comparison with meteorological parameters. Capabilities of the models were compared to each other and the support vector machine was found to be the best performing, based on evaluation criteria. Nonetheless, the decision tree and Bayesian network methods also provided acceptable results. We suggest more studies using the SVM and other methods as hybrids would lead to improved models.
51 citations
Cites methods from "Ground-level O3 sensitivity analysi..."
...According to Tables 3 and 4, SVM yielded a meaningful power in comparison to the other methods in this study and other studies of the writers; hence, application of this modeling system and combining it with other...
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...Specifically, a hybrid of least square and support vector machine or LSSVM ant ic ipa ted to produce potent models ....
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...SVM as the ablest method of this research is selected to run sensitivity analysis....
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...The results indicated that SVM has the best accuracy....
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...Model SVM12 has the optimum results....
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TL;DR: Wang et al. as discussed by the authors developed a method for predicting ozone (O3) concentration based on kernel extreme learning machine (KELM) and support vector machine regression (SVR) and pretreat it by wavelet transformation (WT) and partial least squares (PLS).
46 citations
TL;DR: The proposed hybrid model possesses superlative performance compared to all above-mentioned techniques and achieved exceptional results as the highest R 2, the highest 95% confidence degree, and narrower confidence interval width, are achieved.
Abstract: To accurately predict tropospheric ozone concentration(O3), it is needed to investigate the variety of artificial intelligence techniques’ performance, such as machine learning, deep learning and h...
27 citations
Cites background from "Ground-level O3 sensitivity analysi..."
...…standard environmental predictions performances because of their nonlinear mapping capability, like Support Vector Machine (SVM)(He et al., 2017; Mehdipour & Memarianfard, 2019; Mehdipour et al., 2018; Sumathi & Manivannan, 2020), Extreme Gradient Boosting (XGBoost)(Ma et al., 2020), Bayesian…...
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References
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TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
37,861 citations
08 Feb 1999
TL;DR: Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.
Abstract: Introduction to support vector learning roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik generalization performance of support vector machines and other pattern classifiers, Peter Bartlett and John Shawe-Taylor Bayesian voting schemes and large margin classifiers, Nello Cristianini and John Shawe-Taylor support vector machines, reproducing kernel Hilbert spaces, and randomized GACV, Grace Wahba geometry and invariance in kernel based methods, Christopher J.C. Burges on the annealed VC entropy for margin classifiers - a statistical mechanics study, Manfred Opper entropy numbers, operators and support vector kernels, Robert C. Williamson et al. Part 2 Implementations: solving the quadratic programming problem arising in support vector classification, Linda Kaufman making large-scale support vector machine learning practical, Thorsten Joachims fast training of support vector machines using sequential minimal optimization, John C. Platt. Part 3 Applications: support vector machines for dynamic reconstruction of a chaotic system, Davide Mattera and Simon Haykin using support vector machines for time series prediction, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel. Part 4 Extensions of the algorithm: reducing the run-time complexity in support vector machines, Edgar E. Osuna and Federico Girosi support vector regression with ANOVA decomposition kernels, Mark O. Stitson et al support vector density estimation, Jason Weston et al combining support vector and mathematical programming methods for classification, Bernhard Scholkopf et al.
5,506 citations
TL;DR: A statistically significant association between short-term changes in ozone and mortality on average for 95 large US urban communities, which include about 40% of the total US population, indicates that this widespread pollutant adversely affects public health.
Abstract: Design and Setting Using analytical methods and databases developed for the National Morbidity, Mortality, and Air Pollution Study, we estimated a national average relative rate of mortality associated with short-term exposure to ambient ozone for 95 large US urban communities from 1987-2000. We used distributed-lag models for estimating community-specific relative rates of mortality adjusted for time-varying confounders (particulate matter, weather, seasonality, and long-term trends) and hierarchical models for combining relative rates across communities to estimate a national average relative rate, taking into account spatial heterogeneity. Main Outcome Measure Daily counts of total non–injury-related mortality and cardiovascular and respiratory mortality in 95 large US communities during a 14-year period. Results A 10-ppb increase in the previous week’s ozone was associated with a 0.52% increase in daily mortality (95% posterior interval [PI], 0.27%-0.77%) and a 0.64% increase in cardiovascular and respiratory mortality (95% PI, 0.31%-0.98%). Effect estimates for aggregate ozone during the previous week were larger than for models considering only a single day’s exposure. Results were robust to adjustment for particulate matter, weather, seasonality, and long-term trends. Conclusions These results indicate a statistically significant association between shortterm changes in ozone and mortality on average for 95 large US urban communities, which include about 40% of the total US population. The findings indicate that this widespread pollutant adversely affects public health. JAMA. 2004;292:2372-2378 www.jama.com
1,151 citations
TL;DR: A survey of time series prediction applications using a novel machine learning approach: support vector machines (SVM).
Abstract: Time series prediction techniques have been used in many real-world applications such as financial market prediction, electric utility load forecasting , weather and environmental state prediction, and reliability forecasting. The underlying system models and time series data generating processes are generally complex for these applications and the models for these systems are usually not known a priori. Accurate and unbiased estimation of the time series data produced by these systems cannot always be achieved using well known linear techniques, and thus the estimation process requires more advanced time series prediction algorithms. This paper provides a survey of time series prediction applications using a novel machine learning approach: support vector machines (SVM). The underlying motivation for using SVMs is the ability of this methodology to accurately forecast time series data when the underlying system processes are typically nonlinear, non-stationary and not defined a-priori. SVMs have also been proven to outperform other non-linear techniques including neural-network based non-linear prediction techniques such as multi-layer perceptrons.The ultimate goal is to provide the reader with insight into the applications using SVM for time series prediction, to give a brief tutorial on SVMs for time series prediction, to outline some of the advantages and challenges in using SVMs for time series prediction, and to provide a source for the reader to locate books, technical journals, and other online SVM research resources.
907 citations
University of Leicester1, University of Cambridge2, Cooperative Institute for Research in Environmental Sciences3, Newbury College4, Earth System Research Laboratory5, University of Paris6, University of Edinburgh7, Paul Sabatier University8, Potsdam Institute for Climate Impact Research9, Lancaster University10, King's College London11
TL;DR: In this paper, a review examines current understanding of the processes regulating tropospheric ozone at global to local scales from both measurements and models and takes the view that knowledge across the scales is important for dealing with air quality and climate change in a synergistic manner.
Abstract: Ozone holds a certain fascination in atmospheric science. It is ubiquitous in the atmosphere, central to tropospheric oxidation chemistry, yet harmful to human and ecosystem health as well as being an important greenhouse gas. It is not emitted into the atmosphere but is a by-product of the very oxidation chemistry it largely initiates. Much effort is focussed on the reduction of surface levels of ozone owing to its health impacts but recent efforts to achieve reductions in exposure at a country scale have proved difficult to achieve due to increases in background ozone at the zonal hemispheric scale. There is also a growing realisation that the role of ozone as a short-lived climate pollutant could be important in integrated air quality climate-change mitigation. This review examines current understanding of the processes regulating tropospheric ozone at global to local scales from both measurements and models. It takes the view that knowledge across the scales is important for dealing with air quality and climate change in a synergistic manner.
877 citations