Soil temperature is one of the most important glacio-meteorological parameters that play a critical role in glacier energy and mass balance dynamics, surface hydrological processes, and glacier-atmosphere interaction. However, the availability of the data is acutely scarce in the Himalayan glaciated region. In this study, we applied artificial neural network (ANN) models for the prediction of soil temperature of glacial forefield region of the Pindari Glacier (Central Himalaya). Three-layer feed-forward ANN models were developed and tested for estimating multi-depth soil temperatures using concurrent and antecedent air-soil temperature data for one complete annual cycle as inputs for the models. Models with different combinations of input variables were tested, and best sets of variables were selected based on the prediction accuracy. Rigorous statistics were further employed to compare the performances of different models. High concurrence was obtained between ANN-estimated and measured soil and air temperatures as evident by various correlation coefficients and error ranges. In a boarder perspective, our results point toward the applicability of developed ANN models to provide robust soil temperature prediction for the glacial forefield regions of the Central Himalaya.

土壤温度是最重要的冰川气象参数之一，在冰川能量和质量平衡动力学，地表水文过程以及冰川-大气相互作用中起着至关重要的作用。但是，在喜马拉雅冰河地区，该数据的可用性极为匮乏。在这项研究中，我们将人工神经网络（ANN）模型用于Pindari冰川（喜马拉雅中部）冰川前场区域的土壤温度预测。开发并测试了三层前馈ANN模型，使用一个完整的年度周期的同时和先前的空土温度数据作为模型的输入，以估算多深度土壤温度。测试了具有不同输入变量组合的模型，并根据预测精度选择了最佳变量集。进一步采用严格的统计数据来比较不同模型的性能。通过各种相关系数和误差范围可以明显看出，在ANN估算和测得的土壤和气温之间获得了很高的一致性。从寄宿生的角度来看，我们的结果指向已开发的ANN模型的适用性，以为喜马拉雅中部的冰川前场地区提供可靠的土壤温度预测。