Soil temperature (ST) is one of the crucial variables of soil and it plays a fundamental role in different research scopes such as underground soil physical and agricultural applications. The study explores the modelling performance of a time series‐based model (i.e. bi‐linear, BL), and an artificial intelligence‐based approach including adaptive neuro‐fuzzy inference system (ANFIS), for modelling the daily ST of different soil depths (5, 10, 50 and 100 cm). The study also develops and proposes two diverse types of the hybrid models through coupling the ANFIS with the BL and wavelet analysis (W) to improve the accuracy of the ST modelling. Two stations in Iran (i.e. Isfahan and Urmia) were selected as the study locations. The results demonstrated that the ANFIS generally presented better results than the BL. Furthermore, the hybrid models (i.e. W‐ANFIS and ANFIS‐BL) gave superior performances than the classical ANFIS and BL for modelling the daily ST of the studied areas at various soil depths. In addition to the local evaluation of the ANFIS (i.e. modelling the ST at a specific depth by using the original ST data at that depth), an external analysis was also conducted. In doing so, the daily ST data at a 5 cm depth were modelled via the corresponding ST data at a 10 cm depth, and vice versa. The results denoted the applicability of the ST data at another depth for modelling the ST of each specific/target depth.

中文翻译：

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通过经典模型和混合模型对不同深度的每日土壤温度进行建模

土壤温度（ST）是土壤的关键变量之一，在不同的研究领域（例如地下土壤的物理和农业应用）中发挥着根本作用。这项研究探索了基于时间序列的模型（即双线性，BL）的建模性能，以及基于人工智能的方法，包括自适应神经模糊推理系统（ANFIS），用于对不同土壤深度的每日ST进行建模（ 5、10、50和100厘米）。该研究还通过将ANFIS与BL和小波分析（W）耦合来开发和提出两种不同类型的混合模型，以提高ST建模的准确性。选择了伊朗的两个站点（即伊斯法罕和乌尔米亚）作为研究地点。结果表明，ANFIS通常比BL呈现更好的结果。此外，混合模型（即 与传统的ANFIS和BL相比，W‐ANFIS和ANFIS-BL在针对不同土壤深度的研究区域的每日ST建模方面具有优于经典ANFIS和BL的性能。除了对ANFIS进行局部评估（即使用特定深度的原始ST数据对特定深度的ST建模）外，还进行了外部分析。在此过程中，对5厘米深度的每日ST数据进行了建模通过相应的ST数据（深度为10 cm），反之亦然。结果表明ST数据在另一个深度上的适用性，可用于对每个特定/目标深度的ST进行建模。