A rolling forecast approach for next six-hour air quality index track

Liang, Chen-Jui; Liang, Jeng-Jong; Jheng, Chiao-Wun; Tsai, Ming‐Chang

doi:10.1016/j.ecoinf.2020.101153

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…In Taiwan, November is one of the months of the dry season, and the worst air quality of the year generally occurs in November. However, in East Rift Valley (Taitung), the air quality was relatively low from February to May [ 37 ]. Therefore, the lowest visibility in East Rift Valley (Taitung) occurred in May.…”

Section: Resultsmentioning

confidence: 99%

“…With regard to the sparsely populated areas in eastern Taiwan, the SVM model was the most suitable model for Yilan Alluvial Plain and East Rift Valley. The order of the five target areas in terms of their average annual PM 2.5 concentrations is as follows: Chia-nan Plain (Chiayi) > Taichung Basin (Zhongming) > Juh-miao Hills (Hsinchu) > Yilan Alluvial Plain (Yilan) > East Rift Valley (Taitung) [ 37 ]. Air quality might also be a crucial factor that determines the suitable prediction model for an area.…”

Section: Discussionmentioning

confidence: 99%

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Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

Liang,

Chang,

Hsiao

et al. 2023

Heliyon

Self Cite

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

Liang,

Chang,

Hsiao

et al. 2023

Heliyon

Self Cite

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“…The prediction results would be improved if an error compensation approach could be developed. More accurate prediction results can be obtained by compensating for the errors that are generated during training and combining them with the original prediction results [21].…”

Section: Error Compensation Using Arma Techniquementioning

confidence: 99%

Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing

2022

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Demand forecasting plays a crucial role in a company’s operating costs. Excessive inventory can increase costs and unnecessary waste can be reduced if managers plan for uncertain future demand and determine the most favorable decisions. Managers are demanding increasing accuracy in forecasting as technology advances. Most of the literature discusses forecasting results’ inaccuracy by suspending the model and reloading the data for model retraining and correction, which is extensively employed but causes a bottleneck in practice since users do not have the sufficient ability to correct the model. This study proposes an error compensation mechanism and uses the individuals and moving-range (I-MR) control chart to evaluate the requirement for compensation to solve the current bottleneck using forecasting models. The approach is validated using the case companies’ historical data, and the model is developed using a rolling long short-term memory (LSTM) to output the predicted values; then, five indicators are proposed for screening to determine the prediction statistics to be subsequently employed. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) compare the LSTM, rolling LSTM combined index, and LSTM-autoregressive moving average (ARMA) models. The results demonstrate that the RMSE, MAPE, and MAE of LSTM-ARMA are smaller than those of the other two models, indicating that the error compensation mechanism that is proposed in this study can enhance the prediction’s accuracy.

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“…With the further development of industrialization, a series of environmental pollution problems, such as hazy weather and sandstorms, have caused harm to human physical and mental health. Air quality prediction can scientifically guide people's daily activities and behaviors and improve their quality of life, which is of great significance to environmental monitoring and governance [1,2].…”

Section: Introductionmentioning

confidence: 99%

A New Prediction System Based on Self-Growth Belief Rule Base with Interpretability Constraints

Li¹,

Han²,

Wang³

et al. 2023

Computers, Materials &Amp; Continua

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Prediction systems are an important aspect of intelligent decisions. In engineering practice, the complex system structure and the external environment cause many uncertain factors in the model, which influence the modeling accuracy of the model. The belief rule base (BRB) can implement nonlinear modeling and express a variety of uncertain information, including fuzziness, ignorance, randomness, etc. However, the BRB system also has two main problems: Firstly, modeling methods based on expert knowledge make it difficult to guarantee the model's accuracy. Secondly, interpretability is not considered in the optimization process of current research, resulting in the destruction of the interpretability of BRB. To balance the accuracy and interpretability of the model, a self-growth belief rule base with interpretability constraints (SBRB-I) is proposed. The reasoning process of the SBRB-I model is based on the evidence reasoning (ER) approach. Moreover, the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system. A case study showed that the accuracy and interpretability of the model could be guaranteed. The SBRB-I model has good application prospects in prediction systems.

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A rolling forecast approach for next six-hour air quality index track

Cited by 7 publications

References 15 publications

Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing

A New Prediction System Based on Self-Growth Belief Rule Base with Interpretability Constraints

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