Explainable Deep Learning Model for Carbon Dioxide Estimation
In recent years, environmental sustainability and the reduction of CO2 emissions have become significant research topics. To effectively reduce CO2 emissions, recent studies have used deep learning models to provide precise estimates, but these models often lack interpretability. In light of this, o...
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| Published in | IEICE Transactions on Information and Systems Vol. E108.D; no. 9; pp. 1138 - 1141 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
The Institute of Electronics, Information and Communication Engineers
01.09.2025
一般社団法人 電子情報通信学会 |
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| Online Access | Get full text |
| ISSN | 0916-8532 1745-1361 |
| DOI | 10.1587/transinf.2024EDL8087 |
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| Abstract | In recent years, environmental sustainability and the reduction of CO2 emissions have become significant research topics. To effectively reduce CO2 emissions, recent studies have used deep learning models to provide precise estimates, but these models often lack interpretability. In light of this, our study employs an explainable neural network to learn fuel consumption, which is then converted to CO2 emissions. The explainable neural network includes an explainable layer that can explain the importance of each input variable. Through this layer, the study can elucidate the impact of different speeds on fuel consumption and CO2 emissions. Validated with real fleet data, our study demonstrates an impressive mean absolute percentage error (MAPE) of only 3.3%, outperforming recent research methods. |
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| AbstractList | In recent years, environmental sustainability and the reduction of CO2 emissions have become significant research topics. To effectively reduce CO2 emissions, recent studies have used deep learning models to provide precise estimates, but these models often lack interpretability. In light of this, our study employs an explainable neural network to learn fuel consumption, which is then converted to CO2 emissions. The explainable neural network includes an explainable layer that can explain the importance of each input variable. Through this layer, the study can elucidate the impact of different speeds on fuel consumption and CO2 emissions. Validated with real fleet data, our study demonstrates an impressive mean absolute percentage error (MAPE) of only 3.3%, outperforming recent research methods. |
| ArticleNumber | 2024EDL8087 |
| Author | Xian-Ji ZHANG Lin-Hao HUANG Wei-Juan WANG Chong-Hui LEE Zhen LI Fang-Bin QI |
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| Copyright | 2025 The Institute of Electronics, Information and Communication Engineers |
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| References_xml | – reference: [14] Y. Lou, R. Wu, J. Li, L. Wang, X. Li, and G. Chen, “A Learning Convolutional Neural Network Approach for Network Robustness Prediction,” IEEE Trans. Cybern., vol.53, no.7, pp.4531-4544, July 2023. doi: 10.1109/TCYB.2022.3207878 10.1109/TCYB.2022.3207878 – reference: [11] C.-L. Lo, C.-H. Chen, T.-S. Kuan, K.-R. Lo, and H.-J. Cho, “Fuel Consumption Estimation System and Method with Lower Cost,” Symmetry, vol.9, no.7, article no.105, 2017. doi: 10.3390/sym9070105 10.3390/sym9070105 – reference: [12] L. Zhao, W. Pu, R. Zhou, and Q. Shi, “A Third-Order Majorization Algorithm for Logistic Regression With Convergence Rate Guarantees,” IEEE Signal Process. Lett., vol.31, pp.1700-1704, 2024. doi: 10.1109/LSP.2024.3413306 10.1109/LSP.2024.3413306 – reference: [7] D.W. Otter, J.R. Medina, and J.K. Kalita, “A Survey of the Usages of Deep Learning for Natural Language Processing,” IEEE Trans. Neural Netw. Learn. Syst., vol.32, no.2, pp.604-624, Feb. 2021. doi: 10.1109/TNNLS.2020.2979670 10.1109/TNNLS.2020.2979670 – reference: [5] K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu, Z. Yang, Y. Zhang, and D. Tao, “A Survey on Vision Transformer,” IEEE Trans. Pattern Anal. Mach. Intell., vol.45, no.1, pp.87-110, 1 Jan. 2023. doi: 10.1109/TPAMI.2022.3152247 10.1109/TPAMI.2022.3152247 – reference: [16] A. Faraji, S.A. Sadrossadat, W. Na, F. Feng, and Q.-J. Zhang, “A New Macromodeling Method Based on Deep Gated Recurrent Unit Regularized With Gaussian Dropout for Nonlinear Circuits,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol.70, no.7, pp.2904-2915, July 2023. doi: 10.1109/TCSI.2023.3264616 10.1109/TCSI.2023.3264616 – reference: [21] C. Li, Q. Zhong, and B. Li, “Clustering-Based Neural Network for Carbon Dioxide Estimation,” IEICE Trans. Inf. & Syst., vol.E106-D, no.5, pp.829-832, May 2023. doi: 10.1587/transinf.2022DLL0012 10.1587/transinf.2022DLL0012 – reference: [10] C.-H. Chen, “ExpDNN: Explainable Deep Neural Network,” arXiv, arXiv:2005.03461, pp.1-2, 2020. doi: 10.48550/arXiv.2005.03461 10.48550/arXiv.2005.03461 – reference: [4] S.-H. Lee and H.-C. Ku, “A Dual Attention-Based Recurrent Neural Network for Short-Term Bike Sharing Usage Demand Prediction,” IEEE Trans. Intell. Transp. Syst., vol.24, no.4, pp.4621-4630, April 2023. doi: 10.1109/TITS.2022.3208087 10.1109/TITS.2022.3208087 – reference: [2] T.-H. Tsai and D.-B. Lin, “An On-Chip Fully Connected Neural Network Training Hardware Accelerator Based on Brain Float Point and Sparsity Awareness,” IEEE Open Journal of Circuits and Systems, vol.4, pp.85-98, 2023. doi: 10.1109/OJCAS.2023.3245061 10.1109/OJCAS.2023.3245061 – reference: [3] A. Sayeed, Y. Choi, J. Jung, Y. Lops, E. Eslami, and A.K. Salman, “A Deep Convolutional Neural Network Model for Improving WRF Simulations,” IEEE Trans. Neural Netw. Learn. Syst., vol.34, no.2, pp.750-760, Feb. 2023. doi: 10.1109/TNNLS.2021.3100902 10.1109/TNNLS.2021.3100902 – reference: [6] W. Citko and W. Sienko, “Image Recognition and Reconstruction With Machine Learning: An Inverse Problem Approach,” IEEE Access, vol.11, pp.107463-107471, 2023. doi: 10.1109/ACCESS.2023.3315831 10.1109/ACCESS.2023.3315831 – reference: [9] H. Fang, C. Shi, and C.-H. Chen, “BioExpDNN: Bioinformatic Explainable Deep Neural Network,” 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea (South), pp.2461-2467, 2020. doi: 10.1109/BIBM49941.2020.9313113 10.1109/BIBM49941.2020.9313113 – reference: [20] A.N.T. Kissiedu, G.K. Aggrey, M.G. Asante-Mensah, and A. Asante, “Development of Pneumonia Identification System: A Comparative Analysis of Some Selected CNN Architectures Using Adam, Nadam, and RAdam Optimizers,” 2024 IEEE SmartBlock4Africa, Accra, Ghana, pp.1-12, 2024. doi: 10.1109/SmartBlock4Africa61928.2024 10.1109/SmartBlock4Africa61928.2024 – reference: [15] Y. Qin, H. Fu, F. Xu, and Y. Jin, “EMWP-RNN: A Physics-Encoded Recurrent Neural Network for Wave Propagation in Plasmas,” IEEE Antennas Wireless Propag. Lett., vol.23, no.1, pp.219-223, Jan. 2024. doi: 10.1109/LAWP.2023.3321914 10.1109/LAWP.2023.3321914 – reference: [17] R. Chakraborty and Y. Hasija, “Predicting MicroRNA Sequence Using CNN and LSTM Stacked in Seq2Seq Architecture,” IEEE/ACM Trans. Comput. Biol. Bioinf., vol.17, no.6, pp.2183-2188, 1 Nov.-Dec. 2020. doi: 10.1109/TCBB.2019.2936186 10.1109/TCBB.2019.2936186 – reference: [19] C. Chen, L. Shen, W. Liu, and Z.-Q. Luo, “Efficient-Adam: Communication-Efficient Distributed Adam,” IEEE Trans. Signal Process., vol.71, pp.3257-3266, 2023. doi: 10.1109/TSP.2023.3309461 10.1109/TSP.2023.3309461 – reference: [18] X. Yang, X. Zheng, and H. Gao, “SGD-Based Adaptive NN Control Design for Uncertain Nonlinear Systems,” IEEE Trans. Neural Netw. Learn. Syst., vol.29, no.10, pp.5071-5083, Oct. 2018. doi: 10.1109/TNNLS.2018.2790479 10.1109/TNNLS.2018.2790479 – reference: [1] Y. Tang, C. Zhao, J. Wang, C. Zhang, Q. Sun, W.X. Zheng, W. Du, F. Qian, and J. Kurths, “Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey,” IEEE Trans. Neural Netw. Learn. Syst., vol.34, no.12, pp.9604-9624, Dec. 2023. doi: 10.1109/TNNLS.2022.3167688 10.1109/TNNLS.2022.3167688 – reference: [8] C. Cervellera and D. Macciò, “Local Linear Regression for Function Learning: An Analysis Based on Sample Discrepancy,” IEEE Trans. Neural Netw. Learn. Syst., vol.25, no.11, pp.2086-2098, Nov. 2014. doi: 10.1109/TNNLS.2014.2305193 10.1109/TNNLS.2014.2305193 – reference: [22] C.-H. Chen, “Fuel Consumption Estimation Method Based on Clustering-based Deep Learning Model,” Asia-Pacific Journal of Clinical Oncology, vol.18, no.S2, pp.129-130, Aug. 2022. doi: 10.1111/ajco.13830 10.1111/ajco.13830 – reference: [13] J. Liu and D. Zhou, “Minimum Functional Length Analysis of K-Mer Based on BPNN,” IEEE/ACM Trans. Comput. Biol. Bioinf., vol.19, no.5, pp.2920-2925, 1 Sept.-Oct. 2022. doi: 10.1109/TCBB.2021.3098512 10.1109/TCBB.2021.3098512 |
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| SubjectTerms | Carbon dioxide estimation deep learning explainable neural network fuel consumption |
| Title | Explainable Deep Learning Model for Carbon Dioxide Estimation |
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