This work is executed to predict the variation in global temperature and greenhouse gas (GHG) emissions resulting from climate change and global warming, taking into consideration the natural climate cycle. A mathematical model was developed using a Recurrent Neural Network (RNN) with Long–Short-Term Memory (LSTM) model. Data sets of global temperature were collected from 800,000 BC to 1950 AD from the National Oceanic and Atmospheric Administration (NOAA). Furthermore, another data set was obtained from The National Aeronautics and Space Administration (NASA) climate website. This contained records from 1880 to 2019 of global temperature and carbon dioxide levels. Curve fitting techniques, employing Sin, Exponential, and Fourier Series functions, were utilized to reconstruct both NOAA and NASA data sets, unifying them on a consistent time scale and expanding data size by representing the same information over smaller periods. The fitting quality, assessed using the R-squared measure, ensured a thorough process enhancing the model's accuracy and providing a more precise representation of historical climate data. Subsequently, the time-series data were converted into a supervised format for effective use with the LSTM model for prediction purposes. Augmented by the Mean Squared Error (MSE) as the analyzed loss function, normalization techniques, and refined data representation from curve fitting the LSTM model revealed a sharp increase in global temperature, reaching a temperature rise of 4.8 °C by 2100. Moreover, carbon dioxide concentrations will continue to boom, attaining a value of 713 ppm in 2100. In addition, the findings indicated that the RNN algorithm (LSTM model) provided higher accuracy and reliable forecasting results as the prediction outputs were closer to the international climate models and were found to be in good agreement. This study contributes valuable insights into the trajectory of global temperature and GHG emissions, emphasizing the potential of LSTM models in climate prediction.

中文翻译：

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通过 LSTM 模型预测未来全球温度和温室气体排放


这项工作的目的是预测气候变化和全球变暖导致的全球温度和温室气体（GHG）排放的变化，同时考虑到自然气候循环。使用循环神经网络 (RNN) 和长短期记忆 (LSTM) 模型开发了一个数学模型。美国国家海洋和大气管理局 (NOAA) 收集了从公元前 80 万年到公元 1950 年的全球温度数据集。此外，另一个数据集是从美国国家航空航天局（NASA）气候网站获得的。其中包含 1880 年至 2019 年全球温度和二氧化碳水平的记录。采用正弦、指数和傅里叶级数函数的曲线拟合技术被用来重建 NOAA 和 NASA 数据集，将它们统一在一致的时间尺度上，并通过在更短的时间内表示相同的信息来扩展数据大小。使用 R 平方测量评估的拟合质量确保了全面的过程，提高了模型的准确性并提供了历史气候数据的更精确的表示。随后，时间序列数据被转换为监督格式，以便有效地与 LSTM 模型一起用于预测目的。通过均方误差 (MSE) 进行增强，分析的损失函数、归一化技术以及 LSTM 模型曲线拟合的精炼数据表示揭示了全球气温急剧上升，到 2100 年气温上升了 4.8 °C。此外，碳二氧化氮浓度将继续激增，到 2100 年将达到 713 ppm。 此外，研究结果表明，RNN算法（LSTM模型）提供了更高的准确性和可靠的预测结果，预测输出更接近国际气候模型，并且被发现具有良好的一致性。这项研究为全球气温和温室气体排放轨迹提供了宝贵的见解，强调了 LSTM 模型在气候预测中的潜力。