Estimation of PM10 levels using feed forward neural networks in Igdir, Turkey

Sahin, F.; Işık, Gültekin; Şahin, Gökhan; Kara, Mehmet Kazım

doi:10.1016/j.uclim.2020.100721

Cited by 18 publications

(6 citation statements)

References 28 publications

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“…A reduction in the interior temperature of the panel leads to an improvement in energy efficiency. The Air Pressure map was digitized using the Arc-GIS 10.2 software package and obtained by the application of the "Kriging" method in the Geostatistical Analyst module [ [70] , [71] , [72] ].…”

Section: Methodsmentioning

confidence: 99%

Multi-criteria solar power plant siting problem solution using a GIS-Taguchi loss function based interval type-2 fuzzy approach: The case of Kars Province/Turkey

Sahin,

Akkus,

Koc

et al. 2024

Heliyon

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Section: Methodsmentioning

confidence: 99%

Multi-criteria solar power plant siting problem solution using a GIS-Taguchi loss function based interval type-2 fuzzy approach: The case of Kars Province/Turkey

Sahin,

Akkus,

Koc

et al. 2024

Heliyon

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“…Nonetheless, recent advancements in ANN architectures have significantly enhanced the predictive accuracy of air pollution concentration models 17–19 . In Turkey, various studies have been conducted to forecast air pollutant levels, employing diverse neural network architectures and techniques for handling missing data and applying different neural networks and missing value imputation techniques 20–25 . However, no prior investigations have been undertaken in Turkey to predict PM 2.5 concentrations while systematically comparing the efficacy of different missing data imputation techniques with the RPROP (Resilient Backpropagation) algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…[17][18][19] In Turkey, various studies have been conducted to forecast air pollutant levels, employing diverse neural network architectures and techniques for handling missing data and applying different neural networks and missing value imputation techniques. [20][21][22][23][24][25] However, no prior investigations have been undertaken in Turkey to predict PM 2.5 concentrations while systematically comparing the efficacy of different missing data imputation techniques with the RPROP (Resilient Backpropagation) algorithm. Furthermore, this study delves into the temporal variations of PM 2.5 concentrations and the elucidation of conditional bivariate probability functions (CBPF) for PM 2.5.…”

mentioning

confidence: 99%

Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Koçak

2024

Env Prog and Sustain Energy

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This study analyzed the temporal variation and prediction of fine particulate matter (PM2.5) concentrations in Aksaray, Turkey, a city in Central Anatolia. The relationship between PM2.5 and meteorological parameters such as temperature, humidity, wind speed, and wind direction was investigated. An artificial neural network (ANN) model was developed to predict PM2.5 levels based on meteorological data and air pollutant information. Seasonal and diurnal patterns of PM2.5 concentrations were observed, with higher values recorded during the winter and lower values during the summer. Additionally, higher levels were observed in the morning and evening, while lower levels were recorded in the afternoon. The variations in meteorological parameters, especially temperature and wind speed, significantly influenced PM2.5 levels. To predict hourly PM2.5 concentrations, single and multiple data imputation techniques were employed in combination with resilient back‐propagation (RPROP‐ANN). The neural network was applied, consisting of one input layer comprising 11 parameters, one hidden layer with 20 neurons, and an output layer. The results indicate that the best forecasting performance for PM2.5 was demonstrated by the combination of the missForest imputation technique with the RPROP neural network, as assessed by the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The proposed model is characterized by a low RMSE of 5.94 and a high R2 value of 0.88, demonstrating exceptional predictive performance in air quality.

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“…However, it could not be sufficiently validated for servicing the UVI and expected amount of vitamin D synthesis because the experiment was performed under the limited conditions of specific dates or climate conditions. Further, Fatma et al have estimated the particulate matter (PM10) values by inputting the environmental factors such as nitrogen monoxide (NO) and ozone (O 3 ) by the introduction of neural network technology [ 17 ]. Meanwhile, Jacovide et al introduced a neural network model that calculated the solar UV and the photosynthetically active radiation (PAR) via the input of sunshine fraction, air temperature, and humidity [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User’s Location

Lim

2021

Sensors

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Ultraviolet rays are closely related with human health and, recently, optimum exposure to the UV rays has been recommended, with growing importance being placed on correct UV information. However, many countries provide UV information services at a local level, which makes it impossible for individuals to acquire user-based, accurate UV information unless individuals operate UV measurement devices with expertise on the relevant field for interpretation of the measurement results. There is a limit in measuring ultraviolet rays’ information by the users at their respective locations. Research about how to utilize mobile devices such as smartphones to overcome such limitation is also lacking. This paper proposes a mobile deep learning system that calculates UVI based on the illuminance values at the user’s location obtained with mobile devices’ help. The proposed method analyzed the correlation between illuminance and UVI based on the natural light DB collected through the actual measurements, and the deep learning model’s data set was extracted. After the selection of the input variables to calculate the correct UVI, the deep learning model based on the TensorFlow set with the optimum number of layers and number of nodes was designed and implemented, and learning was executed via the data set. After the data set was converted to the mobile deep learning model to operate under the mobile environment, the converted data were loaded on the mobile device. The proposed method enabled providing UV information at the user’s location through a mobile device on which the illuminance sensors were loaded even in the environment without UVI measuring equipment. The comparison of the experiment results with the reference device (spectrometer) proved that the proposed method could provide UV information with an accuracy of 90–95% in the summers, as well as in winters.

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Estimation of PM10 levels using feed forward neural networks in Igdir, Turkey

Cited by 18 publications

References 28 publications

Multi-criteria solar power plant siting problem solution using a GIS-Taguchi loss function based interval type-2 fuzzy approach: The case of Kars Province/Turkey

Multi-criteria solar power plant siting problem solution using a GIS-Taguchi loss function based interval type-2 fuzzy approach: The case of Kars Province/Turkey

Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User’s Location

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Estimation of PM10 levels using feed forward neural networks in Igdir, Turkey

Cited by 18 publications

References 28 publications

Multi-criteria solar power plant siting problem solution using a GIS-Taguchi loss function based interval type-2 fuzzy approach: The case of Kars Province/Turkey

Multi-criteria solar power plant siting problem solution using a GIS-Taguchi loss function based interval type-2 fuzzy approach: The case of Kars Province/Turkey

Prediction of daily fine particulate matter (PM2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User’s Location

Contact Info

Product

Resources

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Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network