Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India
Abstract 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 mathem...
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crcambridgeupr:10.1017/jog.2021.19 2024-06-23T07:54:14+00:00 Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India Haq, Mohd Anul Azam, Mohd Farooq Vincent, Christian 2021 http://dx.doi.org/10.1017/jog.2021.19 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143021000198 en eng Cambridge University Press (CUP) http://creativecommons.org/licenses/by/4.0/ Journal of Glaciology page 1-14 ISSN 0022-1430 1727-5652 journal-article 2021 crcambridgeupr https://doi.org/10.1017/jog.2021.19 2024-06-12T04:04:46Z Abstract 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 km 3 , respectively. Article in Journal/Newspaper Journal of Glaciology Cambridge University Press Journal of Glaciology 67 264 671 684 |
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Open Polar |
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Cambridge University Press |
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crcambridgeupr |
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English |
description |
Abstract 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 km 3 , respectively. |
format |
Article in Journal/Newspaper |
author |
Haq, Mohd Anul Azam, Mohd Farooq Vincent, Christian |
spellingShingle |
Haq, Mohd Anul Azam, Mohd Farooq Vincent, Christian Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India |
author_facet |
Haq, Mohd Anul Azam, Mohd Farooq Vincent, Christian |
author_sort |
Haq, Mohd Anul |
title |
Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India |
title_short |
Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India |
title_full |
Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India |
title_fullStr |
Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India |
title_full_unstemmed |
Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India |
title_sort |
efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western himalaya, india |
publisher |
Cambridge University Press (CUP) |
publishDate |
2021 |
url |
http://dx.doi.org/10.1017/jog.2021.19 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143021000198 |
genre |
Journal of Glaciology |
genre_facet |
Journal of Glaciology |
op_source |
Journal of Glaciology page 1-14 ISSN 0022-1430 1727-5652 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1017/jog.2021.19 |
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Journal of Glaciology |
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67 |
container_issue |
264 |
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671 |
op_container_end_page |
684 |
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1802646315651301376 |