International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 18, No. 5, 8 Oct. 2026
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Carbon Dioxide Emission Prediction for Trend Analysis using CAViaR Pine Cone Optimization Algorithm Enabled Attention-Based LSTM
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Author(s)
Index Terms
Carbon Dioxide Emission, Trend Analysis, Satellite Image, Long Short-Term Memory, Conditional Autoregressive Value at Risk
Abstract
Predicting Carbon dioxide (CO₂) emissions is essential for guiding environmental policies and climate strategies, yet accurate local-scale prediction remains challenging due to complex spatial and temporal factors. Existing approaches often focus on regional or global models and do not fully exploit high-resolution satellite imagery for local trend analysis. To overcome these limitations, Conditional Pine Cone Optimization enabled Attention-Based Long Short-Term Memory (CPCO_ALSTM) is devised for CO2 Emission prediction for Trend Analysis in Satellite Image. In this approach, satellite images of the query region acquired at different time intervals are used as input. Initially, a Kalman Filter is employed for noise reduction and temporal smoothing during preprocessing. Then, segmentation of satellite image is done using Dense-Res Recurrent Prototypical Network (DRRP-Net), wherein DRRP-Net is developed by combining Dense-Res-Inception Network (DRINet) and Recurrent Prototypical Network (RP-Net). Based on the segmented regions and extracted features, carbon emission estimation is carried out. The estimated CO₂ emissions are then validated against ground-truth emission data obtained from a reference database to assess prediction accuracy. For temporal trend analysis, an Attention-Based Long Short-Term Memory (ALSTM) model is utilized to capture long-range dependencies in emission patterns. The ALSTM parameters are optimally tuned using the proposed CPCO algorithm, which combines Pine Cone Optimization (PCO) with Conditional Autoregressive Value at Risk (CAViaR) to enhance convergence stability and prediction robustness. Additionally, CPCO_ALSTM has attained 96.35% of accuracy, 0.087 of normalized MSE, and 0.957 of Jaccard Coefficient.
Cite This Paper
Choudari Lakshmi, Srinivasa Rao Konda, "Carbon Dioxide Emission Prediction for Trend Analysis using CAViaR Pine Cone Optimization Algorithm Enabled Attention-Based LSTM", International Journal of Information Technology and Computer Science(IJITCS), Vol.18, No.5, pp.138-150, 2026. DOI:10.5815/ijitcs.2026.05.09
Reference
[1]P. Zhou and M. Wang, "Carbon dioxide emissions allocation: A review," Ecological economics, vol. 125, pp. 47-59, 2016.
[2]"Sources and sectoral trend analysis of CO2 emissions data in Nigeria using a modified Mann-Kendall and change point detection approaches.," Energies, vol. 15, no. 3, p. 766, 2022.
[3]A. C. Kone and T. Buke, "Forecasting of CO2 emissions from fuel combustion using trend analysis.," Renewable and Sustainable Energy Reviews, vol. 14, no. 9, pp. 2906-2915, 2010.
[4]C. Lakshmi and S. R. Konda, "DRRP-Net: Dense-Res-Recurrent Prototypical Networks for carbon emission prediction using satellite image time series," Neurocomputing, vol. 639, p. 130216, 2025.
[5]"Energy, environment and sustainable development," Renewable and sustainable energy reviews, vol. 12, no. 9, pp. 2265-2300, 2008.
[6]J. D. L. Brazidec, P. Vanderbecken, A. Farchi, G. Broquet, G. Kuhlmann and M. Bocquet, "Deep learning applied to CO 2 power plant emissions quantification using simulated satellite images," Geoscientific Model Development Discussions, pp. 1-30, 2023.
[7]A. Danjou, G. Broquet, A. Schuh, F.-M. Bréon and T. Lauvaux, "Optimal selection of satellite XCO2 images over cities for urban CO2 emission monitoring using a global adaptive-mesh model," Atmospheric Measurement Techniques Discussions, pp. 1-33, 2023.
[8]B. F. Andrade, M. Buchwitz, M. Reuter, H. Bovensmann, A. Richter, H. Boesch and P. J. Burrows, "A method for estimating localized CO 2 emissions from co-located satellite XCO 2 and NO 2 images," Atmospheric Measurement Techniques, vol. 17, no. 3, pp. 145-1173, 2024.
[9]S. N. Wren, C. A. McLinden, D. Griffin, S.-M. Li, S. G. Cober, A. Darlington, K. Hayden, C. Mihele, R. L. Mittermeier, M. J. Wheeler, M. Wolde and L. Liggio, "Aircraft and satellite observations reveal historical gap between top–down and bottom–up CO2 emissions from Canadian oil sands," PNAS nexus, vol. 2, no. 5, p. pgad140, 2023.
[10]"Bhoonidhi Dataset," [Online]. Available: https://bhoonidhi.nrsc.gov.in/bhoonidhi/index.html. [Accessed September 2025].
[11]"Climate watch dataset," [Online]. Available: climatewatchdata.org/ghgemissions?end_year=2018&gases=all-ghg%2Cco2&start_year=1990. [Accessed September 2025].
[12]B. Wang, Z. Sun, J. Zeng and R. Liu, "Kalman filter and its application in data assimilation," Atmosphere, vol. 14, no. 8, p. 1319, 2023.
[13]H. Tang, X. Liu, S. Sun, X. Yan and X. Xie, "Recurrent mask refinement for few-shot medical image segmentation," in Proceedings of the IEEE/CVF international conference on computer vision, 2021.
[14]L. Chen, P. Bentley, K. Mori, M. Fujiwara and D. Rueckert, "DRINet for medical image segmentation," EEE transactions on medical imaging, vol. 37, no. 11, pp. 2453-2462, 2018.
[15]M. A. Fattah, S. R. Morshed and S. Y. Morshed, "Multi-layer perceptron-Markov chain-based artificial neural network for modelling future land-specific carbon emission pattern and its influences on surface temperature," SN Applied Sciences, vol. 3, no. 3, p. 359, 2021.
[16]Y. Liao, R. Lin and G. Wu, "Attention-based LSTM (AttLSTM) neural network for seismic response modeling of bridges," Computers & Structures, vol. 275, p. 106915, 2023.
[17]M. V. Anaraki and S. Farzin, "The Pine Cone Optimization Algorithm (PCOA)," Biomimetics, vol. 9, no. 2, p. 91, 2024.
[18]I. K. M. Jais and A. R. Ismail, "Adam Optimization Algorithm for Wide and Deep Neural Network," Knowledge Engineering and Data Science, vol. 2, no. 1, p. 41–46, 2019.
[19]F. Mehmood, S. Ahmad and T. K. Whangbo, "An Efficient Optimization Technique for Training Deep Neural Networks," Mathematics, vol. 11, no. 6, 2023.
[20]J. Wu, X.-Y. Chen, H. Zhang, L.-D. Xiong, H. Lei and S.-H. Deng, "Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization," Journal of Electronic Science and Technology, vol. 17, no. 1, pp. 26-40, 2019.