Employing long short-term memory and Facebook prophet model in air temperature forecasting

Toharudin, Toni; Pontoh, Resa Septiani; Caraka, Rezzy Eko; Zahroh, Solichatus; Lee, Youngjo; Chen, Rung Ching

doi:10.1080/03610918.2020.1854302

Cited by 67 publications

(40 citation statements)

References 40 publications

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“…The ADPSO-BiGRU has a small prediction error, so it has a better prediction performance The cell's training process continues until the learning process is completed or a preset halting condition is met. If not a preset halting condition is met, the process is repeated by updating the network's weights, and the neural network calculates the value of each gate function unless it meets the target error [53] ; thus, it must continue to experiment until it discovers the best parameters to achieve the target error. The ADPSO-BiGRU model had a lower RMSE than did the other models and differed considerably from the other three models.…”

Section: Resultsmentioning

confidence: 99%

Air Quality Index Prediction Based on an Adaptive Dynamic Particle Swarm Optimized Bidirectional Gated Recurrent Neural Network–China Region

Wang

et al. 2021

Advcd Theory and Sims

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Accurate predictions of the air quality index (AQI) is critical for pollution control and air quality warning. However, this is challenging because of the nonlinearity of data and the uncertainty between data relationships. This paper proposes a combinatorial model based on an improved adaptive dynamic particle swarm optimization (ADPSO) algorithm to optimize a bidirectional gated recurrent unit (BiGRU) neural network to predict AQI time series and capture data dependence. The ADPSO method incorporates a dynamic spatial search strategy into the standard particle swarm optimization method, allowing the parameters to be dynamically adjusted to balance global and local search capabilities, thus improving the performance and effectiveness of this optimization process. Compared to the BiGRU model, the PSO-BiGRU model, and the radial basis neural,the results of the improved algorithm reveal that the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the ADPSO-BiGRU predicted air pollution index are smaller than the errors of the other three models. The accuracy of the ADPSO-BiGRU prediction model is higher than that of the other models, and it aids in the development of effective regional air quality management policies to reduce the negative impacts of pollution.

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Section: Resultsmentioning

confidence: 99%

Air Quality Index Prediction Based on an Adaptive Dynamic Particle Swarm Optimized Bidirectional Gated Recurrent Neural Network–China Region

Wang

et al. 2021

Advcd Theory and Sims

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show abstract

“…For the next investigation, predictions can be conducted using other input variables related to precipitation, such as B. AUSMI and WNPMI. Then, the prediction can be conducted using other methods, such as the Bayesian approach, Monte Carlo dropout or resampling with the jackknife and bootstrap methods in the form of intervals [53].…”

Section: Discussionmentioning

confidence: 99%

Bandung Rainfall Forecast and Its Relationship with Niño 3.4 Using Nonlinear Autoregressive Exogenous Neural Network

et al. 2022

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The city of Bandung, as the capital city of West Java, is one of several areas in Indonesia with high rainfall. This situation can cause disasters, such as floods and landslides, that can harm many parties. Rainfall in Indonesia, particularly on the island of Java itself, is closely related to the global phenomenon of Niño 3.4. In the period from January 2001–November 2021, the rainfall and Niño 3.4 showed some extreme values. In order to foresee the disasters, an accurate rainfall forecast should be performed. For this reason, we try to construct a model of rainfall forecast and its relation to the global phenomenon of Niño 3.4 using the nonlinear autoregressive exogenous neural network (NARX NN). The result shows that NARX NN (13-7-1) with a Mean Absolute Percentage Error (MAPE) value of 6.26% and R2 of 85.37% is best suited for the prediction of this phenomenon. In addition, this study provides forecast results for the next six periods, which can be used as a reference for the relevant authorities to foresee the possibility of flooding in Bandung city. From the forecast results, it can be concluded that the highest rainfall forecasts in the city of Bandung are in February 2022, and will slowly decrease in March 2022. To prevent hydro-meteorological disasters, such as floods in Bandung city, the community can clear waterways, such as clogged drains, rivers, and dams, as well as prepare tools for evacuation.

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“…Then, in the network validation phase, MAPE is used. Meanwhile, mean absolute error (MAE) (4) and root-mean-square error (RMSE) (5) are used to calculate network accuracy for different models on the same scale [43][44][45][46]. In addition, the RMSE gives a high weight to large errors because errors are squared; thus this metric is useful when large errors are particularly undesirable [47].…”

Section: Metrics Evaluationmentioning

confidence: 99%

National Vaccination and Local Intervention Impacts on COVID-19 Cases

et al. 2021

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COVID-19, as a global pandemic, has spread across Indonesia. Jakarta, as the capital of Indonesia, is the province with the most positive cases. The government has issued various guidelines, both at the central and regional levels. Since it began in 2021, the planned new measures, called ‘Pemberlakuan Pembatasan Kegiatan Masyarakat Darurat’, or PPKM emergency public activity restrictions, began with the possibility that the number of active cases might decrease. Accordingly, global vaccinations were also carried out, as they were in Indonesia. However, the first phase prioritized frontline health workers and high-risk elderly people. This study conducted a causal impact analysis to determine the effectiveness of PPKM in Jakarta and its vaccination program against the increase in daily new cases. Based on this test, PPKM showed a significant effect on the addition of daily new cases and recovered cases. Conversely, the vaccination program only had a significant impact on recovered cases. A forecast of the COVID-19 cases was conducted and indicated that the daily new cases showed a negative trend, although it fluctuated for the next 7 days, while death and recovered cases continued to increase. Hence, it can be said that the vaccination program has still not shown its effectiveness in decreasing the number of daily new cases while PPKM is quite effective in suppressing new cases.

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Employing long short-term memory and Facebook prophet model in air temperature forecasting

Cited by 67 publications

References 40 publications

Air Quality Index Prediction Based on an Adaptive Dynamic Particle Swarm Optimized Bidirectional Gated Recurrent Neural Network–China Region

Air Quality Index Prediction Based on an Adaptive Dynamic Particle Swarm Optimized Bidirectional Gated Recurrent Neural Network–China Region

Bandung Rainfall Forecast and Its Relationship with Niño 3.4 Using Nonlinear Autoregressive Exogenous Neural Network

National Vaccination and Local Intervention Impacts on COVID-19 Cases

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