Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models

Kisi, Özgür; Parmar, Kulwinder Singh; Soni, Kirti; Demir, Vahdettin

doi:10.1007/s11869-017-0477-9

Cited by 71 publications

(17 citation statements)

References 42 publications

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“…Research 592 (2005) pp. [119][120][121][122][123][124][125][126][127][128][129][130][131][132][133][134][135][136][137] models, cell culture experiments, and cell free systems that exposure to diesel exhaust and diesel exhaust particles causes oxidative DNA damage. In this study oxidative stress-induced DNA damage appears to an important mechanism of action of urban particulate air pollution.…”

Section: A/a Authors Year Title Methodology and Results By Particulate mentioning

confidence: 99%

“…There are studies, as is presented in Table 5 that referred to environmental centers where measuring and monitoring key environmental variables and in collaboration with authorities supervise compliance with provisions and regulations [117][118][119][120][121][122][123][124][125][126][128][129][130][131][132][133][134][135][136][137][138][139]. In this important approach a comprehensive review with 108 references referring to the distribution, source, accumulation, transformation, types and toxicity of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) is presented.…”

Section: Related Work For Environmental Centersmentioning

confidence: 99%

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A Pilot Study of an Online Intelligent Environmental Observation System for Monitoring and Evaluating Public Health Hazard from Indoor and Outdoor Pollutants

Kanellopoulos¹,

Ανδρικόπουλος²,

Koutsojannis³

2019

AJEP

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The overall aim of this research is the prevention of environmental risk and impact, through the establishment of a present-day environmental observatory centre, equipped with an intelligent monitoring, management and evaluating system. Detecting the environmental pollutants in a timely manner (forecasting), potentially related to adverse health effects while their frequency appears to have increased, shall contribute crucially to avoiding or reducing further inhabitants' exposure to pollutants in high environmental charging areas. A plurality of data will be collected and analyzed in the pilot Observatory. All these data will be gathered by measuring various environmental parameters (physicochemical, microbiological, ionized and not ionized radiation, dust, noise), a number of which will be transmitted wireless from small scale monitoring stations. The data base (territorial distribution of samples, number and frequency of sampling, analyze results, toxicity and risk indicators of environmental pollutants), will be enriched daily/weekly, with satellite data, meteorological data and satellite photographs (image analysis). With the collection, processing and analysis of measured values for various kinds of pollutants from environmental overloaded surfaces, useful conclusions are drawn regarding to the "pollution tendency" in every different area and the possible effects on human in combination with geography and geomorphology. With the use of artificial intelligence (AI) in human body simulators (whole body phantom models) we intend to make a more accurate qualitative and quantitative forecast: worst-best scenario, about as the life quality for residents-who may be exposed to the pollutants-as the direct or indirect medium to long-term adverse effects.

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Section: A/a Authors Year Title Methodology and Results By Particulate mentioning

confidence: 99%

Section: Related Work For Environmental Centersmentioning

confidence: 99%

A Pilot Study of an Online Intelligent Environmental Observation System for Monitoring and Evaluating Public Health Hazard from Indoor and Outdoor Pollutants

Kanellopoulos¹,

Ανδρικόπουλος²,

Koutsojannis³

2019

AJEP

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“…In recent years, AI techniques have been increasingly used to solve a large number of environmental and water engineering problems. These include evolutionary polynomial regression (EPR) [8,9], ANFIS [10,11], gene expression programming (GEP) [12,13], model tree (MT) [14,15], support vector machine (SVM) [16][17][18], and extreme learning machine (ELM) [8,19]. Various researches have also used AI approaches particularly for river flow forecasting [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

Rezaie-Balf

Nowbandegani

Phillips

et al. 2019

Water

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Accurate prediction of daily streamflow plays an essential role in various applications of water resources engineering, such as flood mitigation and urban and agricultural planning. This study investigated a hybrid ensemble decomposition technique based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) with gene expression programming (GEP) and random forest regression (RFR) algorithms for daily streamflow simulation across three mountainous stations, Siira, Bilghan, and Gachsar, in Karaj, Iran. To determine the appropriate corresponding input variables with optimal lag time the partial auto-correlation function (PACF) and auto-correlation function (ACF) were used for streamflow prediction purpose. Calibration and validation datasets were separately decomposed by EEMD that eventually improved standalone predictive models. Further, the component of highest pass (IMF1) was decomposed by the VMD approach to breakdown the distinctive characteristic of the variables. Results suggested that the EEMD-VMD algorithm significantly enhanced model calibration. Moreover, the EEMD-VMD-RFR algorithm as a hybrid ensemble model outperformed better than other techniques (EEMD-VMD-GEP, RFR and GEP) for daily streamflow prediction of the selected gauging stations. Overall, the proposed methodology indicated the superiority of hybrid ensemble models compare to standalone in predicting streamflow time series particularly in case of high fluctuations and different patterns in datasets.

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“…Datasets collected from Germany, Italy, England and Czech Republic. In the last, the artificial neural network depicted the best result among the others [13]. Kisi et al, used three methods to predict the sulphur dioxide in 3 zones of Delhi, India and the least square support vector machine introduced as the superior method.…”

Section: Introductionmentioning

confidence: 99%

Application of Support Vector Machine and Gene Expression Programming on Tropospheric ozone Prognosticating for Tehran Metropolitan

Mehdipour

Memarianfard

2017

CivileJournal

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Air pollution became fatal issue for humanity and all environment and developed countries unanimously allocated vast investments on monitoring and researches about air pollutants. Soft computing as a novel way for pollutants prediction can be used for measurement tools calibration which can coincidently decrease the expenditures and enhance their ability to adapt quickly. In this paper support vector machine (SVM) and gene expression programming (GEP) as two powerful approaches with reliable results in previous studies, used to predict tropospheric ozone in Tehran metropolitan by using the photochemical precursors and meteorological parameters as predictors. In a comparison between the two approaches, the best model of SVM gave superior results as it depicted the RMSE= 0.0774 and R= 0.8459 while these results of gene expression programming, respectively, are 0.0883 and 0.7938. Sensitivity of O3 against photochemical precursors and meteorological parameters and also for every input parameter, has been analysed discreetly and the gained results imply that PM2.5, PM10, temperature, CO and NO2 are the most effective parameters for O3 values tolerances. For SVM, several kernel tricks used and the best appropriate kernel selected due to its result. Nonetheless, gamma and sin2 values varied for every kernel and in the last radial basis function kernel opted as the best trick in this study. Finally, the best model of both applications revealed, and the resulted models evaluated as reliable and acceptable.

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Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models

Cited by 71 publications

References 42 publications

A Pilot Study of an Online Intelligent Environmental Observation System for Monitoring and Evaluating Public Health Hazard from Indoor and Outdoor Pollutants

A Pilot Study of an Online Intelligent Environmental Observation System for Monitoring and Evaluating Public Health Hazard from Indoor and Outdoor Pollutants

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

Application of Support Vector Machine and Gene Expression Programming on Tropospheric ozone Prognosticating for Tehran Metropolitan

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