利用深度学习和模型推理对环境数据进行插值处理

C. Ibebuchi, Itohan-Osa Abu
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引用次数: 0

摘要

环境数据集的时间分辨率对于从数据中获取信息的粒度起着重要作用。在大多数情况下,需要不同的数据集具有共同的时间分辨率,以便在做出明智决策时对其进行一致的评估和应用。本研究利用深度学习的长短期记忆(LSTM)神经网络和模型推理来提高气候数据集的时间分辨率,特别是温度和降水量,从日尺度到亚日尺度。我们对模型进行了训练,以学习日数据和亚日数据之间的关系,随后将这一知识用于提高时间分辨率更粗的(日)单独数据集的分辨率。我们的研究结果表明,该模型对温度的预测具有很高的准确性,其实际值与亚日预测值之间的相关性为 0.99,平均绝对误差为 0.21 °C。相比之下,该方法对降水的效果较差,解释方差仅为 37%,而对气温的解释方差为 98%。此外,除了对气候数据集进行亚日插值外,我们还调整了方法,利用根据哨兵 2 号归一化差异植被指数预先训练的 LSTM 模型,提高了大地遥感卫星归一化差异植被指数的分辨率(从 16 天间隔提高到 5 天间隔)--后者的时间分辨率相对更高。预测的大地遥感卫星数据和哨兵 1 号数据之间的解释方差为 70%,平均绝对误差为 0.03。这些结果表明,我们的方法特别适用于短期变异性不明显的环境数据集,为提高数据的分辨率和实用性提供了一种有前途的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interpolation of Environmental Data Using Deep Learning and Model Inference
The temporal resolution of environmental data sets plays a major role in the granularity of the information that can be derived from the data. In most cases, it is required that different data sets have a common temporal resolution to enable their consistent evaluations and applications in making informed decisions. This study leverages deep learning with long short-term memory (LSTM) neural networks and model inference to enhance the temporal resolution of climate datasets, specifically temperature, and precipitation, from daily to sub-daily scales. We trained our model to learn the relationship between daily and sub-daily data, subsequently applying this knowledge to increase the resolution of a separate dataset with a coarser (daily) temporal resolution. Our findings reveal a high degree of accuracy for temperature predictions, evidenced by a correlation of 0.99 and a mean absolute error of 0.21 °C, between the actual and predicted sub-daily values. In contrast, the approach was less effective for precipitation, achieving an explained variance of only 37%, compared to 98% for temperature. Further, besides the sub-daily interpolation of the climate data sets, we adapted our approach to increase the resolution of the Normalized difference vegetation index of Landsat (from 16-day to 5-day interval) using the LSTM model pre-trained from the Sentinel 2 Normalized difference vegetation index - that exists at a relatively higher temporal resolution. The explained variance between the predicted Landsat and Sentinel 1 data is 70% with a mean absolute error of 0.03. These results suggest that our method is particularly suitable for environmental datasets with less pronounced short-term variability, offering a promising tool for improving the resolution and utility of the data.
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