利用两级信号分解和深度学习对阿拉斯加永久冻土带的气温进行预测分析

Aymane Ahajjam, Jaakko Putkonen, Emmanuel Chukwuemeka, Robert Chance, T. Pasch
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引用次数: 0

摘要

由于缺乏地面观测站和计算成本高昂,在北极地区居民点以外的地方进行天气预报具有挑战性。在冬季,这些预报对于帮助应对潜在的危险天气条件至关重要,而在春季,这些预报可用于确定每年融雪期间的洪水风险。为此,我们提出了一个混合 VMD-WT-InceptionTime 模型,用于阿拉斯加短期(未来七天)偏远地区气温的多地平线多元预报。首先,采用斯皮尔曼相关系数分析各输入变量与预报目标温度之间的关系。使用变异模式分解(VMD)对输出相关性最强的输入序列进行分解,最后使用小波变换(WT)提取原始输入中固有的时频模式。由此产生的序列被输入一个深度 InceptionTime 模型,用于短期预测。这项混合技术是利用阿拉斯加三个地点 35 年以上的数据开发和评估的。使用深度学习模型(如时间序列转换器、LSTM、MiniRocket)以及统计和传统机器学习基线(如 GBDT、SVR、ARIMA)进行了不同的实验和性能基准测试。所有预测性能都使用四个指标进行评估:均方根误差、平均绝对百分比误差、判定系数和平均方向准确性。使用所提出的混合技术,可以持续获得卓越的预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Analytics of Air Temperature in Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition and Deep Learning
Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weather conditions, while in spring, these forecasts may be used to determine flood risk during annual snow melt. To this end, a hybrid VMD-WT-InceptionTime model is proposed for multi-horizon multivariate forecasting of remote-region temperatures in Alaska over short-term horizons (the next seven days). First, the Spearman correlation coefficient is employed to analyze the relationship between each input variable and the forecast target temperature. The most output-correlated input sequences are decomposed using variational mode decomposition (VMD) and, ultimately, wavelet transform (WT) to extract time-frequency patterns intrinsic in the raw inputs. The resulting sequences are fed into a deep InceptionTime model for short-term forecasting. This hybrid technique has been developed and evaluated using 35+ years of data from three locations in Alaska. Different experiments and performance benchmarks are conducted using deep learning models (e.g., Time Series Transformers, LSTM, MiniRocket), and statistical and conventional machine learning baselines (e.g., GBDT, SVR, ARIMA). All forecasting performances are assessed using four metrics: the root mean squared error, the mean absolute percentage error, the coefficient of determination, and the mean directional accuracy. Superior forecasting performance is achieved consistently using the proposed hybrid technique.
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CiteScore
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