Peramalan Pencemaran Udara Di Kota Surabaya Menggunakan Metode DSARIMA dengan Pendekatan Percentile Error Bootstrap (PEB)

<p>Peramalan pencemaran udara yang akurat diperlukan untuk mengurangi dampak pencemaran udara. Peramalan yang belum akurat akan berdampak kurang efektifnya tindakan yang dilakukan untuk mengantisipasi dampak pencemaran udara. Sehingga diperlukan sebuah pendekatan yang dapat mengetahui keakuratan plot data hasil peramalan. Penelitian ini dilakukan dengan tujuan melakukan peramalan pencemaran udara berdasarkan parameter PM₁₀, NO₂, CO, SO₂, dan O₃dengan metode DSARIMA. Data dalam penelitian ini sebanyak 8.760 data yang berasal dari Dinas Lingkungan Hidup Kota Surabaya. Berdasarkan hasil peramalan selama 168 jam kadar parameter PM₁₀, NO₂, SO₂ dan O₃ cenderung menurun. Hasil peramalan selama 168 jam dengan menggunakan DSARIMA memberikan hasil peramalan yang nilainya mendekati data aktual terbukti dari polanya yang sesuai atau mirip dengan grafik plot data aktual dengan hasil ramalan. Dengan pendekatan PEB, selisih antara data aktual dan data ramalan kecil dan plot grafik PEB mengikuti plot grafik di data aktual, sehingga dapat dikatakan bahwa model sudah sesuai. Hasil akurasi terbaik yang dihasilkan adalah model DSARIMA dengan RMSE terkecil 0,59 didapatkan dari parameter CO yaitu ARIMA(0,1,[1,2,3])(0,1,1)²⁴(0,1,1)¹⁶⁸.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Accurate air pollution forecasting is needed to reduce the impact of air pollution. Inaccurate forecasting will result in less effective actions taken to anticipate the impact of air pollution. So we need an approach that can determine the accuracy of the forecast data plot. This research was conducted with the aim of forecasting air pollution based on the PM₁₀, NO₂, CO, _SO2, and O₃ parameters using the DSARIMA method. The data in this study were 8.760 data from the Surabaya City Environmental Service. Based on the results of forecasting for 168 hours, the levels of PM₁₀, NO_2,SO₂, and O₃ parameters tend to decrease. Forecasting results for 168 hours using DSARIMA provide forecasting results whose values are close to the actual data as evidenced by the pattern that matches or is similar to the actual data plot graph with the forecast results. With the PEB approach, the difference between the actual data and the forecast data is small and the PEB graph plot follows the graph plot in the actual data, so it can be said that the model is appropriate. The best accuracy result is DSARIMA with the smallest RMSE 0,59 obtained from the CO parameter, namely </em>ARIMA(0,1,[1,2,3])(0,1,1)²⁴(0,1,1)¹⁶⁸.</p><p> </p><p> </p>

An Outlier-Robust Point and Interval Forecasting System for Daily PM2.5 Concentration

Air pollution forecasting, particularly of PM2.5 levels, can be used not only to deliver effective warning information to the public but also to provide support for decisions regarding the control and treatment of air pollution problems. However, there are still some challenging issues in air pollution forecasting that urgently need to be solved, such as how to handle and model outliers, improve forecasting stability, and correct forecasting results. In this context, this study proposes an outlier-robust forecasting system to attempt to tackle the abovementioned issues and bridge the gap in the current research. Specifically, the system developed consists of two parts that deal with point and interval forecasting, respectively. For point forecasting, a data preprocessing module is proposed based on outlier handling and data decomposition to mitigate the negative influences of outliers and noise, which can also help the model capture the main characteristics of the original time series. Meanwhile, an outlier-robust forecasting module is designed for better modeling of the preprocessed data. For the model to further improve its accuracy, a nonlinear correction module based on an error ensemble strategy is developed that can provide more accurate forecasting results. Finally, the interval forecasting part of the system is based on a newly proposed artificial intelligence–based distribution evaluation and the results of the point forecasting part to present the range of future changes. Experimental results and analysis utilizing daily PM2.5 concentration from two provincial capital cities in China are discussed to verify the superiority and effectiveness of the system developed, which can be considered an effective technique for point and interval forecasting of daily PM2.5 concentration.

Evaluation of an Air Pollution Forecasting System Based on Micro-Pulse Lidar Cruising Measurements in the South China Sea

In this study, we evaluated the performance of an air pollution forecasting system during a scientific cruise in the South China Sea (SCS) from 9 August to 7 September 2016. The air pollution forecasting system consisted of a Lagrangian transport and dispersion model, the flexible particle dispersion model (FLEXPART), coupled with a high-resolution Weather Research and Forecasting model (WRF). The model system generally reproduced the meteorological variability and reasonably simulated the distribution of aerosols both vertically and horizontally along the cruise path. The forecasting system was further used to study the regional transport of non-local aerosols over the SCS and track its sources during the cruise. The model results showed that Southeast Asia contributed to more than 90% of the non-local aerosols over the northern region of the SCS due to the southwesterly prevailing winds. Specifically, the largest mean contribution was from Vietnam (39.6%), followed by Thailand (25.1%). This study indicates that the model system can be applied to study regional aerosols transport and provide air pollution forecasts in the SCS.

Spatiotemporal Graph Convolutional Recurrent Neural Network Model for Citywide Air Pollution Forecasting

This paper applies a Spatiotemporal Graph Convolutional Recurrent Neural Network which is a tight combination of a Graph Neural Network (GNN) to a Recurrent Neural Network (RNN) architecture for air pollution forecasting in long-term for the entire city. Our model can effectively learn the spatial and temporal features of the air pollution data and its influential factors (e.g. weather, traffic, external areas) at the time. Our method achieves better performance than a state-of-the-art ConvLSTM model in air pollution forecasting and a hybrid GNN-based model that separates GNN and RNN in discrete layers.

Spatiotemporal Graph Convolutional Recurrent Neural Network Model for Citywide Air Pollution Forecasting

This paper applies a Spatiotemporal Graph Convolutional Recurrent Neural Network which is a tight combination of a Graph Neural Network (GNN) to a Recurrent Neural Network (RNN) architecture for air pollution forecasting in long-term for the entire city. Our model can effectively learn the spatial and temporal features of the air pollution data and its influential factors (e.g. weather, traffic, external areas) at the time. Our method achieves better performance than a state-of-the-art ConvLSTM model in air pollution forecasting and a hybrid GNN-based model that separates GNN and RNN in discrete layers.