Prediction of air pollutants for air quality using deep learning methods in a metropolitan city

Daş, Bihter; Dursun, Ömer Osman; Toraman, Suat

doi:10.1016/j.uclim.2022.101291

Cited by 21 publications

(8 citation statements)

References 26 publications

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“…R-squared is used to determine how well a model generalizes. Low R-squared values may indicate that the model has overfitting problems and fits new data poorly [8,[24][25][26]. In this study, we aim to find the predictive modeling closest to our experimental results, to see the deviations from the experimental values, and to capture the relationships between a dependent variable and an independent variable.…”

Section: Evaluation Criteriamentioning

confidence: 99%

An artificial intelligence model estimation for functionalized graphene quantum dot-based diode characteristics

Öter,

Ersöz,

Berktaş

et al. 2024

Phys. Scr.

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The use of artificial intelligence (AI) approaches, one of the most significant technological advances of the 21st century, to determine the diode parameters that can be obtained from Schottky diode characterization allows data to be collected, processed, analyzed, and results obtained faster than ever before, with high accuracy. It also supports the development of a selection and modeling tool for future studies and, most importantly, facilitates modeling implementation with faster and fewer experimental results. In this context, this study presents a machine learning (ML) model to predict diode parameters from current-voltage (I-V) measurements of polyethyleneimine-functionalized graphene quantum dots (GQDs)-based Schottky hybrid diode. This study used K-Nearest Neighbor, Random Forest, Multilayered Perceptron, and Support Vector Machine algorithms. In research, the lowest model error of each model was compared, and the performance of the models obtained was evaluated. In addition, out of 30 diodes on the fabricated structure, the diode with the best rectification ratio(RR) was identified within a few seconds using machine learning, verified to be the same as the diode selected by the researchers, and the optimal estimation of RR and ideality factor(n) was made from the diode parameters using the thermionic emission (TE)method. The experimental results were compared with machine learning models. Among these algorithms, the RF algorithm performed best with a root mean square error (RMSE)value of 4.1 E-05 and an R-squared value of(R2) 0.999998. The success of RF depends on the characteristics of the dataset used, its size, and data distribution. RF reduces overfitting by taking the average of multiple decision trees and is less sensitive to noise and outliers in the data. The results obtained will allow the development of a selection and modeling tool for future studies and, most importantly, facilitate modeling faster and with fewer experimental results, enabling the integration of AI into science.

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Section: Evaluation Criteriamentioning

confidence: 99%

An artificial intelligence model estimation for functionalized graphene quantum dot-based diode characteristics

Öter,

Ersöz,

Berktaş

et al. 2024

Phys. Scr.

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“…It is mentioned that the hybrid model has a greater success rate in estimating. Das et al 48 apply multilayer perceptron (MLP), RNN, and LSTM methods separately to PM 10 and SO 2 concentration data of the Başakşehir district of Istanbul province. It is revealed that the LSTM method produced the best outcome.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM₁₀ concentrations

Taşağıl Arslan,

Kılıç Depren

2024

Journal of Chemometrics

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The rapid advancement of industrialization and urbanization has led to the global problem of air pollution. Air quality can decrease due to pollutants in the air, including types of gases and particles that are carcinogenic, causing adverse health effects. Therefore, estimating the concentration of air pollutants is of great interest as it can provide accurate information about air quality with proper planning of future activities. In this manner, this study considers Istanbul, a province with a high concentration of industry, population, and vehicle traffic. Particulate matter (PM), one of the most basic air pollutants, is stated to contain microscopic solids or liquid droplets that are small enough to be inhaled and cause serious health problems. Thus, it is recommended to apply discrete wavelet transform (DWT) and deep learning method long short‐term memory (LSTM) as a hybrid model to predict the concentration of PM10. Using the mentioned methods, they can predict air pollution to have been developed within the scope of this study. Furthermore, the hybrid approach with LSTM by selecting the most appropriate discrete wavelet type emphasizes the difference of this study from the existing literature. The ability of these developed methods to make successful future predictions helps institutions and organizations that can take precautions on the subject to take action at the right time; in addition, the deep learning methods used contribute to the development of sustainable smart environmental systems. In today's environment when air pollution is increasing and threatening human health, any precaution that can be taken would improve the quality of life for all living things, reduce health issues and deaths caused by air pollution, and thus raise the degree of well‐being. These findings might offer a reliable scientific evidence for Istanbul City's air pollution management, which can serve as an example for other regions.

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“…Das et al [57] compared the performance of MLP, RNN, and LSTM models in predicting air pollutants such as PM10 and SO 2 . The evaluation metrics used were MSE, RMSE, MAE, and R2.…”

Section: Related Workmentioning

confidence: 99%

Impact of Green Energy Transportation Systems on Urban Air Quality: A Predictive Analysis Using Spatiotemporal Deep Learning Techniques

Mumtaz,

Amin,

Khan

et al. 2023

Energies

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Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning models, including long short-term memory (LSTM) and bidirectional LSTM, and machine learning models, including decision tree and random forest regression, were utilized to forecast the levels of NO2, SO2, and CO in the atmosphere. Our results demonstrate that implementing green energy transportation systems in urban centers of developing countries can enhance air quality by approximately 98%. Notably, the bidirectional LSTM model outperformed others in predicting NO2 and SO2 concentrations, while the LSTM model excelled in forecasting CO concentration. These results offer valuable insights into predicting air pollution levels and guiding green energy policies to mitigate the adverse health effects of air pollution.

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Prediction of air pollutants for air quality using deep learning methods in a metropolitan city

Cited by 21 publications

References 26 publications

An artificial intelligence model estimation for functionalized graphene quantum dot-based diode characteristics

An artificial intelligence model estimation for functionalized graphene quantum dot-based diode characteristics

Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM₁₀ concentrations

Impact of Green Energy Transportation Systems on Urban Air Quality: A Predictive Analysis Using Spatiotemporal Deep Learning Techniques

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Prediction of air pollutants for air quality using deep learning methods in a metropolitan city

Cited by 21 publications

References 26 publications

An artificial intelligence model estimation for functionalized graphene quantum dot-based diode characteristics

An artificial intelligence model estimation for functionalized graphene quantum dot-based diode characteristics

Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM10 concentrations

Impact of Green Energy Transportation Systems on Urban Air Quality: A Predictive Analysis Using Spatiotemporal Deep Learning Techniques

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Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM₁₀ concentrations