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Prediction of Daily Temperature Based on the Robust Machine Learning Algorithms

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  • Yu Li

    (Alibaba Cloud Big Data Application College, Zhuhai College of Science and Technology, Zhuhai 519041, China)

  • Tongfei Li

    (Alibaba Cloud Big Data Application College, Zhuhai College of Science and Technology, Zhuhai 519041, China
    Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR 999078, China)

  • Wei Lv

    (Alibaba Cloud Big Data Application College, Zhuhai College of Science and Technology, Zhuhai 519041, China)

  • Zhiyao Liang

    (Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao SAR 999078, China)

  • Junxian Wang

    (Alibaba Cloud Big Data Application College, Zhuhai College of Science and Technology, Zhuhai 519041, China)

Abstract

Temperature climate is an essential component of weather forecasting and is vital in predicting future weather patterns. Accurate temperature predictions can assist individuals and organizations in preparing for potential weather-related events such as heat waves or cold snaps. However, achieving precise temperature predictions necessitates thoroughly comprehending the underlying factors influencing climate patterns. The study utilized two models, LSTM and DLSTM, to forecast daily air temperature using 1097 data points gathered from central and southern regions of Tabriz city of Iran in Asia from 2017 to 2019. The results indicated that the proposed model had a high accuracy rate for predicting daily air temperature for test data, with RMSE DLSTM = 0.08 °C and R-Square DLSTM = 0.99. The DLSTM algorithm is known for its high speed, accuracy, time series prediction, noise reduction capabilities for data, the large volume of data processing, and improved performance of predicted data. In summary, while both LSTM and DLSTM are used for predicting data points, DLSTM is a more advanced version that includes multiple layers of memory cells and is better suited for handling complex sequences of events.

Suggested Citation

  • Yu Li & Tongfei Li & Wei Lv & Zhiyao Liang & Junxian Wang, 2023. "Prediction of Daily Temperature Based on the Robust Machine Learning Algorithms," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9289-:d:1166732
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    References listed on IDEAS

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    2. Richard Tol, 2002. "Estimates of the Damage Costs of Climate Change. Part 1: Benchmark Estimates," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 21(1), pages 47-73, January.
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