ANALYSIS AND PREDICTION OF MOTOR VEHICLE CARBON DIOXIDE EMISSIONS USING A HYBRID LSTM AND ARIMA ALGORITHM

Muhammad Hakam Fardana; Wahyu Syaifullah Jauharis Saputra; Made Hanindia Prami Swari

doi:10.23960/jitet.v13i3.6782

Authors

Muhammad Hakam Fardana Universitas Pembangunan Nasional Veteran Jawa Timur
Wahyu Syaifullah Jauharis Saputra Universitas Pembangunan Nasional "Veteran" Jawa Timur
Made Hanindia Prami Swari Universitas Pembangunan Nasional "Veteran" Jawa Timur

DOI:

https://doi.org/10.23960/jitet.v13i3.6782

Abstract Views: 52 File Views: 40

Keywords:

Motor Vehicle CO2 Emissions, Emission Prediction, Hybrid Algorithm LSTM and ARIMA, Forecasting

Abstract

CO2 emissions from motor vehicles contribute substantially to climate change. Accurate prediction of emission trends is thus crucial for mitigation strategies. This research evaluates the performance of a Hybrid Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) model for predicting Motor Vehicle CO2 Emissions. This hybrid model integrates ARIMA's capability in handling linear patterns and LSTM's in capturing long-term non-linear dependencies. Using 1000 historical data entries from the Eco-Route Application, the hybrid model was tested and compared with single models. Results show the hybrid model achieved good prediction accuracy with MAE 0.0941, MAPE 10.20%, and RMSE 0.1081 in its best scenario. However, on this specific dataset, the single ARIMA model demonstrated the best overall performance (MAE 0.0835, MAPE 9.33%, RMSE 0.0975). Dataset limitations were identified as affecting the hybrid's capability. The Hybrid LSTM-ARIMA model is determined to be a promising option for CO2 emission prediction, especially when larger datasets are available.

Downloads

Download data is not yet available.

References

T. Wen, Y. Liu, Y. he Bai, and H. Liu, “Modeling and forecasting CO2 emissions in China and its regions using a novel ARIMA-LSTM model,” Heliyon, vol. 9, no. 11, Nov. 2023.

IPCC, “2019 Refinement to The 2006 IPSS Guidelines for National Greenhouse Gas Inventories,” 2019.

S. Anwar et al., “INDONESIA Third Biennial Update Report Under the United Nations Framework Convention on Climate Change REPUBLIC OF INDONESIA Coordinating Lead Authors Acknowledgement: Ministry of Environment and Forestry would like to thank to Ministry of Energy and Mineral Resources,” 2021. [Online]. Available: http://www.ditjenppi.menlhk.go.id.

D. Castells-Quintana, E. Dienesch, and M. Krause, “Density, cities and air pollution: a global view,” 2020. [Online]. Available: https://ssrn.com/abstract=3713325.

K. Mehmood et al., “The impact of COVID-19 pandemic on air pollution: a global research framework, challenges, and future perspectives,” Environmental Science and Pollution Research, vol. 29, no. 35, pp. 52618–52634, Jul. 2022, doi: 10.1007/s11356-022-19484-5.

European Environment Agency, The European Environment : State and outlook 2015. Publications Office, 2015.

Rismawati Sihombing, “KAJIAN KEBUTUHAN JALUR HIJAU SEBAGAI PENYERAP EMISI KARBON DIOKSIDA KENDARAAN BERMOTOR DI SEPANJANG JALAN ARTERI MANOKWARI,” 2012.

H. Sukarto, J. Teknik, H. Tower, L. Karawaci, Tangerang, and Banten, “PEMILIHAN MODEL TRANSPORTASI di DKI JAKARTA dengan ANALISIS KEBIJAKAN ‘PROSES HIRARKI ANALITIK,’” vol. 3, Apr. 2006.

H. Yulianto, S. Iwan, and N. Daulay, “Analisis Efektivitas Metode Forecasting Terhadap Permintaan Produk Pt Arara Abadi Perawang Effectiveness Analysis Method Of Forecasting Product Demand Pt Arara Abadi Perawang,” 2014.

T. Aljuneidi, S. Punia, A. Jebali, and K. Nikolopoulos, “Forecasting and planning for a critical infrastructure sector during a pandemic: Empirical evidence from a food supply chain,” Eur J Oper Res, vol. 317, no. 3, pp. 936–952, 2024, doi: https://doi.org/10.1016/j.ejor.2024.04.009.

S. Rahayu and D. H. Nurdiansyaha, “Analisis Peramalan Penjualan Produk Kaos Sablon (Studi Kasus: Home Industry Alva Cloth),” Jurnal Ilmiah Wahana Pendidikan, vol. 8, no. 22, pp. 383–393, 2022, doi: 10.5281/zenodo.7349799.

A. A. T. Susilo, R. Kurniawan, and G. P. Julyansa, “PENERAPAN MODEL REGRESI LINEAR BERGANDA PADASISTEM PREDIKSI PRODUKSI HASIL PERTANIAN(PADI) DI KOTA LUBUKLINGGAU DENGAN BAHASA PEMROGRAMAN PYTHON,” 2024.

Fungki Wahyu and Billy Hendrik, “Perbandingan Algoritma Time Series Dan Fuzzy Inference System Dalam Analisis Data Deret Waktu,” Jurnal Penelitian Teknologi Informasi dan Sains, vol. 1, no. 3, pp. 16–24, Aug. 2023, doi: 10.54066/jptis.v1i3.711.

N. Mukaromah, “ANALISIS FORECASTING (PERAMALAN) PERMINTAANKARET PADA PT PERKEBUNAN NUSANTARA XII BANJARSARI JEMBER,” 2019.

N. Zucchet and A. Orvieto, “Recurrent neural networks: vanishing and exploding gradients are not the end of the story,” May 2024, [Online]. Available: http://arxiv.org/abs/2405.21064.

S. Siami-Namini, N. Tavakoli, and A. Siami Namin, “A Comparison of ARIMA and LSTM in Forecasting Time Series,” in Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, Institute of Electrical and Electronics Engineers Inc., Jul. 2018, pp. 1394–1401. doi: 10.1109/ICMLA.2018.00227.

S. Wahyuningsih and dan Desi Yuniarti, “Penerapan Metode ARIMA Ensembel pada Peramalan (Studi Kasus: Inflasi di Indonesia) Application of ARIMA Ensemble Method in forecasting (Case Study: Inflation in Indonesia),” Jurnal EKSPONENSIAL, vol. 7, no. 1, 2016.

Z. Liu et al., “The Characteristics of ARMA (ARIMA) Model and Some Key Points to Be Noted in Application: A Case Study of Changtan Reservoir, Zhejiang Province, China,” Sustainability (Switzerland), vol. 16, no. 18, Sep. 2024, doi: 10.3390/su16187955.