Long-term Land Surface Temperature Forecasting in Different Climate Zones using Long Short-term Memory
Keywords:
recurrent neural networks, long short-term memory, long-term temperature forecasting, temperature regime, climatic zones, land surface temperatureAbstract
Climate change, which has been observed for several decades, is becoming increasingly widespread. The consequences of such changes are a negative impact on ecosystems in different regions of the planet, as well as on the biosphere. The Sustainable Development Goals define climate action as one of the key goals, which covers a wide range of actions to avoid and mitigate the consequences caused by climate change. To preserve biodiversity, increase the safety of residents of cities and communities located in vulnerable regions, it is necessary to form an integrated approach that will ensure sustainable development and improve the quality of life. Understanding the future climate situation and potential consequences is impossible without high-quality forecasting of climate indicators. One of the main indicators is the temperature of the Earth’s surface. The article analyzes the use of a recurrent neural network with a long short-term memory for long-term forecasting of temperature in different climatic zones. A study of the forecast accuracy for different time periods, considering climatic zoning, was conducted. The results indicate the feasibility of using the proposed approach for forecasting future Earth surface temperatures based on historical data.
References
World Meteorological Organization. Available online: https://wmo.int/ (accessed on 21 February 2025).
M. Perera, J. De Hoog, K. Bandara, D. Senanayake, & S. Halgamuge, “Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data,” Applied Energy, vol. 361, 122971, 2024. https://doi.org/10.1016/j.apenergy.2024.122971.
T. Hovorushchenko, V. Alekseiko, & V. Levashenko, “Machine learning methods’ comparison for land surface temperatures forecasting due to climate classification,” Proceedings of the 1st International Workshop on Advanced Applied Information Technologies AdvAIT 2024 with CEUR-WS, Khmelnytskyi, Ukraine, Zilina, Slovakia, December 5, 2024, vol. 3899, pp. 55-68. https://ceur-ws.org/Vol-3899/paper7.pdf.
T. Hovorushchenko, & V. Alekseiko, “Land surface temperature forecasting in the context of the development of sustainable cities and communities,” Computer Systems and Information Technologies, vol. 3, pp. 6–12, 2024. https://doi.org/10.31891/csit-2024-3-1.
V. Alekseiko, “Web-based information system for land surface temperature forecasting using machine learning methods,” Science and technology today, vol. 10, issue 38, pp. 17–27, 2024. https://doi.org/10.52058/2786-6025-2024-10(38)-17-27.
V. Ivanenko, V. Alekseiko, O. Klymenko, “Machine forecast evaluation: an algorithm for trends, financial indicators, and business process optimization,” Science and Technology Today, vol. 8, issue 36, pp. 1018–1033, 2024. https://doi.org/10.52058/2786-6025-2024-8(36)-1018-1033.
K. Zhang et al., “Self-supervised learning for time series analysis: Taxonomy, progress, and prospects,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 10, pp. 6775-6794, 2024. https://doi.org/10.1109/TPAMI.2024.3387317.
O. Duda, et al., “Data processing in IoT for smart city systems,” Proceedings of the 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Metz, France, 2019, pp. 96-99. https://doi.org/10.1109/IDAACS.2019.8924262.
S. Lin, W. Lin, W. Wu, H. Chen, & J. Yang, “SparseTSF: Modeling long-term time series forecasting with 1k parameters,” arXiv (Cornell University), 2024. https://doi.org/10.48550/arxiv.2405.00946.
Y. Gong, Y. Zhang, F. Wang, & C. Lee, “Deep learning for weather forecasting: A CNN-LSTM hybrid model for predicting historical temperature data,” arXiv (Cornell University), 2024. https://doi.org/10.48550/arxiv.2410.14963.
P. Akhila, R. L. S. Anjana and M. Kavitha, “Climate forecasting: Long short term memory model using global temperature data,” Proceedings of the 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2022, pp. 469-473. https://doi.org/10.1109/ICCMC53470.2022.9753779.
K. Zhou, C. Zhang, B. Xu, J. Huang, C. Li, & Y. Pei, “TE-LSTM: a prediction model for temperature based on multivariate time series data,” Remote Sensing, vol. 16, issue 19, 3666, 2024. https://doi.org/10.3390/rs16193666.
A. Badugu, K. S. Arunab, & A. Mathew, “Predicting land surface temperature using data-driven approaches for urban heat island studies: a comparative analysis of correlation with environmental parameters,” Modeling Earth Systems and Environment, vol. 10, issue 1, pp. 1043–1076, 2023. https://doi.org/10.1007/s40808-023-01822-2.
C. B. Pande, J. C. Egbueri, R. Costache, L. M. Sidek, Q. Wang, F. Alshehri, N. M. Din, V. K. Gautam, & S. C. Pal, “Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development,” Journal of Cleaner Production, vol. 444, 141035, 2024. https://doi.org/10.1016/j.jclepro.2024.141035.
S. Jafarian-Namin, D. Shishebori, and A. Goli, “Analyzing and predicting the monthly temperature of Tehran using ARIMA model, artificial neural network, and its improved variant,” Journal of Applied Research on Industrial Engineering, vol. 11, issue 1, pp. 76-92, 2024. ttps://doi.org/10.22105/jarie.2023.356297.1502.
X. Chen, Z. Jiang, H. Cheng, et al., “A novel global average temperature prediction model based on GM-ARIMA combination model,” Earth Sci Inform, vol. 17, pp. 853–866, 2024. https://doi.org/10.1007/s12145-023-01179-1.
M. Elseidi, “A hybrid Facebook Prophet-ARIMA framework for forecasting high-frequency temperature data,” Model. Earth Syst. Environ., vol. 10, pp. 1855–1867, pp. 2024. https://doi.org/10.1007/s40808-023-01874-4.
Wang, G., Su, H., Mo, L., Yi, X., & Wu, P. (2024). Forecasting of soil respiration time series via clustered ARIMA. Computers and Electronics in Agriculture, 225, 109315. https://doi.org/10.1016/j.compag.2024.109315.
R. R. Guerra, A. Vizziello, P. Savazzi, E. Goldoni, & P. Gamba, “Forecasting LoRaWAN RSSI using weather parameters: A comparative study of ARIMA, artificial intelligence and hybrid approaches,” Computer Networks, vol. 243, 110258, 2024. https://doi.org/10.1016/j.comnet.2024.110258.
O. Soprun, M. Bublyk, Yu. Matseliukh, V. Andrunyk, L. Chyrun, I. Dyyak, A. Yakovlev, M. Emmerich, O. Osolinsky, A. Sachenko, “Forecasting temperatures of a synchronous motor with permanent magnets using machine learning,” Proceedings of the 2nd International Workshop on Modern Machine Learning Technologies and Data Science (MoMLeT+DS 2020), Volume I: Main Conference Lviv-Shatsk, Ukraine, June 2-3, 2020, pp. 95-120. https://ceur-ws.org/Vol-2631/paper8.pdf.
H. Jdi, & N. Falih, “Comparison of time series temperature prediction with auto-regressive integrated moving average and recurrent neural network,” International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, vol. 14, no. 2, 1770, 2024. https://doi.org/10.11591/ijece.v14i2.pp1770-1778
O. Bezsonov, et al., “Using a deep learning neural network to predict flight path,” Radioelectronic and Computer Systems, vol. 2025, no. 1, pp. 71-84, 2025. https://doi.org/10.32620/reks.2025.1.05.
X. Zhang, H. Xiang, X. Xiong, Y. Wang, & Z. Chen, “Benchmarking core temperature forecasting for lithium-ion battery using typical recurrent neural networks,” Applied Thermal Engineering, vol. 248, 123257, 2024. https://doi.org/10.1016/j.applthermaleng.2024.123257.
W. Shah, J. Chen, I. Ullah, M. H. Shah, & I. Ullah, “Application of RNN-LSTM in predicting drought patterns in Pakistan: A pathway to sustainable water resource management,” Water, vol. 16, issue 11, 1492, 2024. https://doi.org/10.3390/w16111492.
O. Tolstoluzka, D. Telezhenko, “Development and training of LSTM models for control of virtual distributed systems using TensorFlow and Keras,” Radioelectronic and Computer Systems, vol. 2024, no. 3, pp. 27-37, 2024. https://doi.org/10.32620/reks.2024.3.02.
O. A. Alawi, H. M. Kamar, R. Z. Homod, & Z. M. Yaseen, “Deep learning and tree-based models for earth skin temperature forecasting in Malaysian environments,” Applied Soft Computing, vol. 155, 111411, 2024. https://doi.org/10.1016/j.asoc.2024.111411.
V. Farhangmehr, H. Imanian, A. Mohammadian, J. H. Cobo, H. Shirkhani, & P. Payeur, “A spatiotemporal CNN-LSTM deep learning model for predicting soil temperature in diverse large-scale regional climates,” The Science of the Total Environment, vol. 968, 178901, 2025. https://doi.org/10.1016/j.scitotenv.2025.178901.
M. King, S. I. Woo, & C. Yune, “Utilizing a CNN-RNN machine learning approach for forecasting time-series outlet fluid temperature monitoring by long-term operation of BHEs system,” Geothermics, vol. 122, 103082, 2024. https://doi.org/10.1016/j.geothermics.2024.103082.
S. Bamati, H. Chaoui and H. Gualous, “Enhancing battery thermal management with virtual temperature sensor using hybrid CNN-LSTM,” IEEE Transactions on Transportation Electrification, vol. 10, no. 4, pp. 10272-10287, 2024. https://doi.org/10.1109/TTE.2024.3376515.
J. Shen, W. Wu, & Q. Xu, “Accurate prediction of temperature indicators in Eastern China using a multi-scale CNN-LSTM-Attention model,” arXiv (Cornell University), 2024. https://doi.org/10.48550/arxiv.2412.07997.
I. Uluocak, M. Bilgili, “Daily air temperature forecasting using LSTM-CNN and GRU-CNN models,” Acta Geophys., vol. 72, pp. 2107–2126, 2024. https://doi.org/10.1007/s11600-023-01241-y.
Y. Zahidi, Y. Al-Amrani, Y. El Younoussi, “Deep learning CNN–LSTM hybrid approach for arabic sentiment analysis using word embedding models,” International Journal of Modern Education and Computer Science (IJMECS), vol. 17, no. 1, pp. 72-90, 2025. https://doi.org/10.5815/ijmecs.2025.01.06.
J. Y. Ahn, Y. Kim, H. Park, S. H. Park, & H. K. Suh, “Evaluating time-series prediction of temperature, relative humidity, and CO2 in the greenhouse with transformer-based and RNN-based models,” Agronomy, vol. 14, issue 3, 417, 2024. https://doi.org/10.3390/agronomy14030417.
Y. Fu, J. Song, J. Guo, Y. Fu, & Y. Cai, “Prediction and analysis of sea surface temperature based on LSTM-transformer model,” Regional Studies in Marine Science, vol. 78, 103726, 2024. https://doi.org/10.1016/j.rsma.2024.103726.
S. O. Abter, S. M. Jameel, H. M. Majeed, & A. H. Sabry, “Intelligent forecasting temperature measurements of solar PV cells using modified recurrent neural network,” EUREKA Physics and Engineering, vol. 3, pp. 169–177, 2024. https://doi.org/10.21303/2461-4262.2024.003354.
Humid Continental (Group D), Physical Geography. (n.d.). [Online]. Available at: https://courses.lumenlearning.com/suny-geophysical/chapter/humid-continental-group-d/.
Kaggle. Globallandtemperature, 2018. [Online]. Available at: https://www.kaggle.com/datasets/sambapython/globallandtemperature. (accessed on 11 February 2025).
Global Historical Weather and Climate Data, Weather and Climate, 2025. [Online]. Available at: https://weatherandclimate.com/. (accessed on 15 February 2025).
M. Waqas, & U. W. Humphries, “A critical review of RNN and LSTM variants in hydrological time series predictions,” MethodsX, vol. 13, 102946, 2024. https://doi.org/10.1016/j.mex.2024.102946.
N. Yehia, & N. Yehia, “Understanding long short-term memory (LSTM) networks,” Machine Learning Archive, 2024. [Online]. Available at: https://mlarchive.com/deep-learning/understanding-long-short-term-memory-networks/.
GeeksforGeeks, What is LSTM long short term memory? 2024. [Online]. Available at: https://www.geeksforgeeks.org/deep-learning-introduction-to-long-short-term-memory/ (accessed on 10 January 2025).
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
