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Simulating active layer temperature based on weather factors on the Qinghai–Tibetan Plateau using ANN and wavelet-ANN models
Cold Regions Science and Technology ( IF 4.1 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.coldregions.2020.103118
Siru Gao , Qingbai Wu , Zhongqiong Zhang , Guanli Jiang

Abstract Active layer temperature (ALT) is an important dynamic attribute in characterization of permafrost change. Accurate simulation of the dynamic changes of ALT is essential for management and application of monitoring ALT data. This paper discusses the use of artificial neural network (ANN) and wavelet-ANN (W-ANN) hybrid models to simulate and forecast the ALT time series data based on five weather factors: air temperature, precipitation, wind speed, downward longwave radiation and downward shortwave radiation. Data are available for cold (FH1 site) and warm (CM2 site) permafrost locations on the Qinghai–Tibetan Plateau for the period 1996–2010. The ANN-based and W-ANN-based ALT models are developed using data from 1996 to 2007 and ALT forecasts are produced for the period 2008–2010 at both sites. The results demonstrate that ANN and W-ANN models can precisely simulate the ALT. The W-ANN hybrid model that uses decomposed sub-series as input provides forecasting results that are more accurate than the ANN model, which uses original time series. Moreover, the W-ANN-based ALT model is found more appropriate for modeling complicated physical relations between inputs and output.

中文翻译:

使用人工神经网络和小波人工神经网络模型基于天气因素模拟青藏高原活动层温度

摘要 活动层温度(ALT)是表征多年冻土变化的一个重要动态属性。准确模拟ALT的动态变化对于监测ALT数据的管理和应用至关重要。本文讨论了使用人工神经网络(ANN)和小波-ANN(W-ANN)混合模型模拟和预测基于五个天气因素的ALT时间序列数据:气温、降水、风速、向下长波辐射和向下的短波辐射。1996-2010 年青藏高原寒冷(FH1 站点)和温暖(CM2 站点)永久冻土位置的数据可用。基于 ANN 和基于 W-ANN 的 ALT 模型是使用 1996 年至 2007 年的数据开发的,并且在两个站点生成 2008-2010 年期间的 ALT 预测。结果表明,ANN 和 W-ANN 模型可以精确地模拟 ALT。使用分解子序列作为输入的 W-ANN 混合模型提供的预测结果比使用原始时间序列的 ANN 模型更准确。此外,发现基于 W-ANN 的 ALT 模型更适合建模输入和输出之间的复杂物理关系。
更新日期:2020-09-01
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