Boosting Algorithm to Handle Unbalanced Classification of PM<sub>2.5</sub> Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

Toharudin, Toni; Caraka, Rezzy Eko; Pratiwi, Indah Reski; Kim, Yun‐Ho; Gio, Prana Ugiana; Sakti, Anjar Dimara; Noh, Maengseok; Nugraha, Farid Azhar Lutfi; Pontoh, Resa Septiani; Putri, Tafia Hasna; Azzahra, Thalita Safa; Cerelia, Jessica Jesslyn; Darmawan, Gumgum; Pardamean, Bens

doi:10.1109/access.2023.3265019

Cited by 10 publications

(3 citation statements)

References 95 publications

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“…Machine learning algorithms such as Support Vector Regression (SVR) [42]- [44], Random Forest (RF) [45]- [48], Extreme Gradient Boosting (XGBoost) [49]- [52], and Artificial Neural Networks (ANNs) have shown their applicability to air quality prediction. Among thease, conventional statistical models such as SVR [53]- [55], RF Pipeline (RFP) [11], [56], [57], ARIMA [58], [59], Seasonal ARIMA(SARIMA) [60], [61], and Multi-Layer Perceptron (MLP) [62]- [65] have lower prediction evaluation values than technology-based neural network methods [66]- [69].…”

Section: Methodsmentioning

confidence: 99%

“…The Kemayoran area in Jakarta shows that throughout June 2022, the average concentration of PM2.5 was 41 µg/m 3 , which is included in the moderate category. Specifically, the Kemayoran area contributed the highest pollution with 169 US AQI, equal to 90 µg/ m 3 , followed by Pejaten Barat with 155 US AQI or 63.2 µg/ m 3 [11]. In third place, the US Embassy in Central Jakarta touched 153 US AQI or 59.3 µg/ m 3 .…”

Section: Introductionmentioning

confidence: 96%

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Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Putri,

Caraka,

Toharudin

et al. 2024

IEEE Access

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Particulate matter forecasting is fundamental for early warning and controlling air pollution, especially PM2.5. The increase in this level of concentration will lead to a negative impact on public health. This study develops a hybrid model of CNN-LSTM and CONV-LSTM by combining a convolutional neural network (CNN) with an LSTM network to forecast PM2.5 concentration for the next few hours in Kemayoran DKI Jakarta, which is known as a busy area. We discovered the advantages of CNN in effectively extracting features and LSTM in learning long-term historical data from PM2.5 concentration time series data. The predictive model of CNN-LSTM is carried out in a different architecture where the CNN process is carried out first to become the input of LSTM. For CONV-LSTM, it is carried out in one architecture where the multiplication in the LSTM architecture is coupled with the convolution process. This research will explain how the method of developing hybrid CNN-LSTM and CONV-LSTM in predicting PM2.5 concentrations. Based on metric evaluation, the two models are compared to find the best model. Both predictive models produce MAPE values that fall into the good enough category with values <20%. Results were obtained for CONV-LSTM with MAE worth 6.52, RMSE 8.55, and MAPE 16.39%. As a result, the CONV-LSTM model performs better than CNN-LSTM in nowcasting PM2.5.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Putri,

Caraka,

Toharudin

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…The integration of conventional ML and deep learning (DL) techniques into air quality assessment has yielded positive outcomes [34], [35]. Decision trees (DTs) have gained popularity in air quality evaluation, with various decision tree algorithms showing impressive outcomes in research on air quality prediction [36]- [39].…”

Section: Related Workmentioning

confidence: 99%

Toward Cleaner Industries: Smart Cities’ Impact on Predictive Air Quality Management

Chatterjee,

Raju,

Navya Thara

et al. 2024

IEEE Access

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The Smart City (SC) framework has garnered global recognition for its transformative influence on society through innovative solutions. However, the extensive use of Internet of Things (IoT) devices in SCs raises concerns regarding electronic waste and resource consumption. Addressing this challenge necessitates integrating smart grid systems to safeguard SC residents' environment and wellbeing. Accurate air quality prediction is essential for informed societal decisions, safe transportation, and disaster preparedness. This study introduces a novel approach: Towards Cleaner Industries: Smart Cities' Impact on Predictive Air Quality Management (SPAM). The SPAM model utilizes a bidirectional stacking mechanism of long short-term memory neural networks, considering spatiotemporal correlations to forecast future air pollutant concentrations. Surpassing conventional methods, SPAM enhances accuracy while reducing computational complexity. Experimental findings demonstrate enhanced efficiency and accuracy, underscoring its practicality in industrial contexts. The SPAM model represents a significant advancement in promoting environmental sustainability within the SC framework.

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Software development effort estimation using boosting algorithms and automatic tuning of hyperparameters with Optuna

Hassanali,

Soltanaghaei,

Javdani Gandomani

et al. 2024

J Software Evolu Process

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Considering the increasing need for software projects, estimating software development efforts is essential and can lead to improved project delivery quality. Machine learning methods are widely used to improve the accuracy of estimation. The boosting method is an ensemble machine learning technique less used in this field. In this research, five boosting algorithms including Adaboost, Gradient boosting, XGBoost, LightGBM, and CatBoost were implemented with the hyperparameter tuning framework Optuna on the ISBSG database. The Optuna is a next‐generation optimization method for automatically tuning hyperparameters of algorithms. Six evaluation criteria MMRE, MdMRE, MAE, MSE, Pred(0.25), and SA were used to evaluate the findings. The results show that the hyperparameter automatic tuning by Optuna increases the accuracy of prediction provided by all five models. When the Catboost algorithm uses Optuna to tune its hyperparameters has made the best prediction among the five algorithms studied in this research. Using Optuna, compared to the case where the algorithm uses its default settings, the highest percentage of prediction improvement was observed in the XGBoost algorithm (except for the SA criterion). Based on the criteria of MMRE, Pred(0.25), and SA, this study has a better prediction than some relatively similar articles.

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Boosting Algorithm to Handle Unbalanced Classification of PM_2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 10 publications

References 95 publications

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Toward Cleaner Industries: Smart Cities’ Impact on Predictive Air Quality Management

Software development effort estimation using boosting algorithms and automatic tuning of hyperparameters with Optuna

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Boosting Algorithm to Handle Unbalanced Classification of PM2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 10 publications

References 95 publications

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM2.5 Level

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM2.5 Level

Toward Cleaner Industries: Smart Cities’ Impact on Predictive Air Quality Management

Software development effort estimation using boosting algorithms and automatic tuning of hyperparameters with Optuna

Contact Info

Product

Resources

About

Boosting Algorithm to Handle Unbalanced Classification of PM_2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level