Knowledge of glacier volume is crucial for ice flow modelling and predicting the impacts of climate change on glaciers. Rugged terrain, harsh weather conditions and logistic costs limit field-based ice thickness observations in the Himalaya. Remote-sensing applications, together with mathematical models, provide alternative techniques for glacier ice thickness and volume estimation. The objective of the present research is to assess the application of artificial neural network (ANN) modelling coupled with remote-sensing techniques to estimate ice thickness on individual glaciers with direct field measurements. We have developed two ANN models and estimated the ice thickness of Chhota Shigri Glacier (western Himalaya) on ten transverse cross sections and two longitudinal sections. The ANN model estimates agree well with ice thickness measurements from a ground-penetrating radar, available for five transverse cross sections on Chhota Shigri Glacier. The overall root mean square errors of the two ANN models are 24 and 13 m and the mean bias errors are ±13 and ±6 m, respectively, which are significantly lower than for other available models. The estimated mean ice thickness and volume for Chhota Shigri Glacier are 109 ± 17 m and 1.69 ± 0.26 km3, respectively.

中文翻译：

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用于冰川冰厚估计的人工神经网络的效率：印度喜马拉雅西部的案例研究

冰川体积的知识对于冰流建模和预测气候变化对冰川的影响至关重要。崎岖的地形、恶劣的天气条件和物流成本限制了在喜马拉雅山进行实地冰层厚度观测。遥感应用与数学模型一起为冰川冰厚度和体积估计提供了替代技术。本研究的目的是评估人工神经网络 (ANN) 建模与遥感技术相结合的应用，以通过直接现场测量来估计单个冰川上的冰层厚度。我们开发了两个人工神经网络模型，并估计了Chhota Shigri Glacier（喜马拉雅西部）的十个横断面和两个纵断面的冰厚度。人工神经网络模型估计与来自探地雷达的冰厚度测量结果非常吻合，可用于 Chhota Shigri 冰川的五个横向横截面。两个 ANN 模型的总均方根误差分别为 24 和 13 m，平均偏差误差分别为 ±13 和 ±6 m，显着低于其他可用模型。Chhota Shigri 冰川的估计平均冰厚度和体积分别为 109 ± 17 m 和 1.69 ± 0.26 km3， 分别。