AI-based air quality PM2.5 forecasting models for developing countries: A case study of Ho Chi Minh City, Vietnam

Rakholia, Rajnish; Le, Quan; Vu, Khue Hoang Ngoc; Ho, Bang Quoc; Carbajo, Ricardo Simón

doi:10.1016/j.uclim.2022.101315

Cited by 15 publications

(5 citation statements)

References 15 publications

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“…SGDRegressor is a regression algorithm that uses stochastic gradient descent (SGD) to minimize the loss function and is suitable for handling large-scale datasets. Compared with the traditional batch gradient descent, SGDRegressor approximates the E(w, b) true gradient by considering only one training sample per iteration, resulting in an efficient training process [45]. Due to the stochastic nature of the decline, SGDRegressor requires more iterations to reach the minimum, but it is still computationally inexpensive.…”

Section: Sgdregressor Algorithmmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold

Zhan-jun

Shi

Zou

et al. 2023

Sensors

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Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators (WTGs), and the existing methods are insufficient to achieve accurate and rapid fault diagnosis of WTGs, and the operation and maintenance costs of WTGs are too high. To invent a new method for fast and accurate fault diagnosis of WTGs, this study constructs a stacking integration model based on the machine learning algorithms light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and stochastic gradient descent regressor (SGDRegressor) using publicly available datasets from Energias De Portugal (EDP). This model is automatically tuned for hyperparameters during training using Bayesian tuning, and the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the model to determine its applicability and accuracy. The fitted residuals of the test set were calculated, the Pauta criterion (3σ) and the temporal sliding window were applied, and a final adaptive threshold method for accurate fault diagnosis and alarming was created. The model validation results show that the adaptive threshold method proposed in this study is better than the fixed threshold for diagnosis, and the alarm times for the GENERATOR fault type, GENERATOR_BEARING fault type, and TRANSFORMER fault type are 1.5 h, 5.8 h, and 3 h earlier, respectively.

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Section: Sgdregressor Algorithmmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold

Zhan-jun

Shi

Zou

et al. 2023

Sensors

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“…Artificial intelligence algorithms are playing an increasingly important role in environmental detection. For example, in hydrology, artificial intelligence, and climate detection, the use of corresponding algorithms can achieve good prediction results [9][10][11].…”

Section: Related Workmentioning

confidence: 99%

A Novel Combined Model for Air Quality Index Forecasting in Changchun

Chen,

Wang,

Deng

2023

Atmosphere

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With the rapid development of the economy and continuous improvement in people’s living standards, the predictions of the air quality index have attracted wide attention. In this paper, a new feature selection method (Pearson-MI) and a combined model construction method (modified inverse variance method) were proposed to study the air quality index (AQI) and its influencing factors in Changchun. The Pearson-MI method selects the factors that affect the AQI of Changchun City from many influencing factors. This method reduces the RMSE of the LSTM model and XGBoost model by 27% and 5% and the MAE by 41% and 5%, respectively. A model that combines XGBoost, SVR, RF, and LSTM was constructed using the inverse variance method to predict the air quality index of Changchun City. The modified combined model resulted in a 2% reduction in RMSE and a 0.6% reduction in MAE compared with the unmodified combined model. The numerical results of our study show that the prediction accuracy of the modified combined model is obviously higher than that of the basic model, and the prediction accuracy is further improved under the Pearson-MI feature selection.

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“…P M 2.5 (µg/m 3 ) DO (mg/L) Matrix GCN-GRU Rakholia et al [11] GCN-GRU Huan et al [ In this paper, we compare the performance of our implemented model with other studies, which can be observed in Table 2. For P M 2.5 , Rakholia's study [11] implemented a 1D CNN-LSTM model to predict P M 2.5 values after 24 hours and evaluated prediction accuracy using MAE and RMSE. Our GCN-GRU model shows a lower performance of around 0.9 in MAE and 0.87 in RMSE, indicating a higher prediction accuracy.…”

Section: Performance Comparisonmentioning

confidence: 99%

Hybrid Deep Learning-Based Air and Water Quality Prediction Model

Yoon,

Yu,

lee

2023

ESANN 2023 Proceesdings

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This paper analyzes the impact of surrounding data on predicting air and water pollution levels by incorporating relevant features and examining their influence. By doing so, we can confirm the relationship between air and water pollution. A hybrid deep learning-based model is trained and various datasets and models are compared and analyzed. The proposed GCN-GRU model achieved the best results not only for P M2.5 but also for Dissolved Oxygen. The hybrid model takes into account the spatial and temporal effects of data characteristics and provides more accurate environmental prediction information through correlation analysis.

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AI-based air quality PM2.5 forecasting models for developing countries: A case study of Ho Chi Minh City, Vietnam

Cited by 15 publications

References 15 publications

Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold

Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold

A Novel Combined Model for Air Quality Index Forecasting in Changchun

Hybrid Deep Learning-Based Air and Water Quality Prediction Model

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