PREDICT URBAN AIR POLLUTION IN SURABAYA USING RECURRENT NEURAL NETWORK – LONG SHORT TERM MEMORY
Abstract
Air is one of the primary needs of living things. If the condition of air is polluted, then the lives of humans and other living things will be disrupted. So it is needed to perform special handling to maintain air quality. One way to facilitate the prevention of air pollution is to make air pollutionforecasting by utilizing past data. Through the Environmental Office, the Surabaya City Government has monitored air quality in Surabaya every 30 minutes for various air quality parameters including CO, NO, NO2, NOx, PM10, SO2 and meteorological data such as wind direction, wind direction, wind speed, wind speed, global radiation, humidity, and air temperature. These data are very useful to build a prediction model for the forecast of air pollution in the future. With the large amount and variance of data generated from monitoring air quality in Surabaya city, a qualified algorithm is needed to process it. One algorithm that can be used is Recurrent Neural Network - Long Short Term Memory (RNN-LSTM). RNN-LSTM is built for sequential data processing such as time-series data. In this study, several analyses are performed. There are trend analysis, correlation analysis of pollutant values to meteorological data, and predictions of carbon monoxide pollutants using the Recurrent Neural Network - LSTM in the city of Surabaya correlated with meteorological data. The results of this study indicate that the best prediction model using RNN-LSTM with RMSE calculation gets an error of 1,880 with the number of hidden layer 2 and epoch 50 scenarios. The predicted results built can be used as a reference in determining the policy of the city government to deal with air pollution going forward.
Full Text:
PDFReferences
M. E. Marlier, A. S. Jina, P. L. Kinney, R. S. DeFries, "Extreme Air Pollution in Global Megacities," Current Climate Change Reports, vol. 2, pp. 15-27, 2016.
Health Effects Institute. 2018. State of Global Air 2018. Special Report. Boston, MA:Health Effects Institute.
World Health Organization, "World health statistics 2018: monitoring health for the SDGs," Sustainable development goals, Geneva, 2018.
Badan Pusat Statistik.Jumlah Kendaraan Bermotor. Available: https://www.bps.go.id/linkTableDinamis/view/id/1133
A. Syahrani, "Analisis Kinerja Mesin Bensin Berdasarkan Hasil Uji Emisi," SMARTek, vol. 4, no. 4, 2006.
A. Caragliu, C. Del Bo, and P. Nijkamp, "Smart cities in Europe," Serie Research Memoranda 0048, VU University Amsterdam, 2009.
C. Benevolo, R. P. Dameri, B. D’Auria, "Smart Mobility in Smart City," in Lecture Notes in Information Systems and Organisation, vol. 11, Springer, Cham, 2016.
A. Bhati, M. Hansen, and C. M. Chan, "Energy conservation through smart homes in a smart city: A lesson for Singapore households," Energy Policy. vol. 104, pp. 230-239, 2017.
S. Suakanto, S. H. Supangkat, Suhardi, and R. Saragih, "Smart city dashboard for integrating various data of sensor networks," in Proc. International Conference on ICT for Smart Society, 2013.
N. Suri, Z. Zielinski, M. Tortonesi, C. Fuchs, M. Pradhan, K. Wrona, J. Furtak, D. B. Vasilache, M. Street, V. Pellegrini, G. Benincasa, A. Morelli, C. Stefanelli, E. Casini, and M. Dyk, "Exploiting smart city IoT for disaster recovery operations," in Proc. IEEE World Forum on Internet of Things, pp. 458-463, 2018.
E. Al Nuaimi, H. Al Neyadi, N. Mohamed, and J. Al Jaroodi "Applications of big data to smart cities," J. Internet Serv Appl., vol 6, no. 25, 2015.
H. Kumar, M. K. Singh, M. P. Gupta, and J. Madaan. "Moving towards smart cities: Solutions that lead to the Smart City Transformation Framework". Technological Forecasting & Social Change, vol. 153, 2020.
Sumayang, Lalu. 2003. Dasar-Dasar Manajemen Produksi dan Operasi. Salemba Empat,Jakarta.
Supranto J. 2000. Statistik (Teori dan Aplikasi), Edisi Keenam. Jakarta: Erlangga.
Heizer, Jay dan Render, Barry. 2009. Manajemen Operasi, Buku 1 Edisi 9. Jakarta: Salemba Empat.
X. Zhang, M. H. Chen, and Y. Qin, "NLP-QA Framework Based on LSTM-RNN," in Proc. International Conference on Data Science and Business Analytics, pp. 307-311, 2018.
S. Abujar, A. K. M. Masum, S. M. M. H. Chowdhury, M. Hasan, and S. A. Hossain, "Bengali Text generation Using Bi-directional RNN," in Proc. International Conference on Computing, Communication and Networking Technologies, pp. 1-5, 2019.
H. Xie, M. A. Bin Ahmadon, S. Yamaguchi, and I. Toyoshima, "Random Sampling and Inductive Ability Evaluation of Word Embedding in Medical Literature," in Proc. IEEE International Conference on Consumer Electronics, pp. 1-4, 2019.
S. Wang, P. Zhou, W. Chen, J. Jia, and L. Xie, "Exploring RNN-Transducer for Chinese speech recognition," in Proc. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 1364-1369, 2019.
M. Jiang, Z. Yang, and C. Zhao, "What to play next? A RNN-based music recommendation system," in Proc. Asilomar Conference on Signals, Systems, and Computers, pp. 356-358, 2017.
Y. Hu and S. Lin, "Deep Reinforcement Learning for Optimizing Finance Portfolio Management," in Proc. Amity International Conference on Artificial Intelligence, pp. 14-20, 2019.
M. Rozenwald, E. Khrameeva, G. Sapunov, and M. Gelfand, "Prediction of 3D Chromatin Structure Using Recurrent Neural Networks," in Proc. IEEE International Conference on Bioinformatics and Biomedicine, pp. 2488-2488, 2018.
A. Xiao, J. Liu, Y. Li, Q. Song, and N. Ge, "Two-phase rate adaptation strategy for improving real-time video QoE in mobile networks," China Communications, vol. 15, no. 10, pp. 12-24, 2018.
M. W. P. Aldi, J. Jondri, and A. Aditsania, "Analisis Dan Implementasi Long Short Term Memory Neural Network untuk Prediksi Harga Bitcoin," eProceedings of Engineering, vol. 5, no. 2, 2018.
S. Taneja, N. Sharma, K. Oberoi, and Y. Navoria, "Predicting trends in air pollution in Delhi using data mining," in Proc. India International Conference on Information Processing, pp. 1-6, 2016.
V. Chaudhary, A. Deshbhratar, V. Kumar, and D. Paul, "Time Series Based LSTM Model to Predict Air Pollutant's Concentration for Prominent Cities in India," 2018.
V. Reddy, P. Yedavalli, S. Mohanty, and U. Nakhat, "Deep Air: Forecasting Air Pollution in Beijing, China", Environmental Science, 2018.
S. Siami-Namini, N. Tavakoli, and A. S. Namin, "A Comparison of ARIMA and LSTM in Forecasting Time Series," in Proc. IEEE International Conference on Machine Learning and Applications, pp. 1394-1401, 2018.
R. B. Kurdikeri and A. B. Raju, "Comparative Study of Short-Term Wind Speed Forecasting Techniques Using Artificial Neural Networks," in Proc. International Conference on Current Trends towards Converging Technologies, pp. 1-5, 2018.
R. Achkar, F. Elias-Sleiman, H. Ezzidine, and N. Haidar, "Comparison of BPA-MLP and LSTM-RNN for Stocks Prediction," in Proc. International Symposium on Computational and Business Intelligence, pp. 48-51, 2018.
Keputusan Menteri Negara Lingkungan Hidup Nomor : KEP 45 / MENLH / 1997 Tentang Indeks Standar Pencemar Udara
Keputusan Kepala Badan Pengendalian Dampak Lingkungan No. 107 Tahun 1997 Tanggal 21 November 1997
Y. Bengio, P. Simard, and P. Fransconi, "Learning Long-Term Dependencies with Gradient Descent is Difficult" IEEE Transaction on Neural Networks, vol. 5, no. 2, 1994.
S. Makridakis, A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, and R. Winkler, "The Accuracy of Extrapolative (Time Series Methods): Results of a Forecasting Competition," Journal of Forecasting, vol. 1, no. 2, pp. 111-153, 1982.
A. Gulli and S. Pal. 2017. Deep Learning with Keras. Mumbai: Packt Publishing.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting," Journal of Machine Learning Research, vol. 15. 1929-1958, 2014.
DOI: http://dx.doi.org/10.12962/j24068535.v18i2.a988
Refbacks
- There are currently no refbacks.
