Abstract
Climate change can have adverse effects on various ecosystems on the globe, with the cryosphere being affected to a significant extent. Of the cryosphere, mountain or alpine glaciers are essential resources for freshwater and various ecosystem services. Glacial ablation is the process of removal of snow and ice from a glacier, which includes melting, evaporation, and erosion. The increase in temperature on the Earth due to climate changes is causing rapid glacial abrasion. The rapid global decline in alpine glaciers makes it necessary to identify the key drivers responsible for a glacial retreat to understand the eventual modifications to the surroundings and the Earth’s ecosystem. This study attempts to understand the influence of different driving factors leading to glacier retreat using Machine Learning (ML) and Remote Sensing (RS) techniques. Three models have been developed to estimate the glacial retreat: Feedforward Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM). The RNN performed the best with an average training and validation accuracy of 0.9. The overall shift of the area estimate has been identified over 10 years. The model thus generated can lead to a better understanding of the region and can provide a baseline for policy and mitigation strategies in the future.
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References
Angstrom A 1925 The albedo of various surfaces of ground; Geografiska Annaler 7(4) 323–342, https://doi.org/10.1080/20014422.1925.11881121.
Baraka S, Akera B, Aryal B, Sherpa T, Shresta F, Ortiz A and Bengio Y 2020 Machine learning for glacier monitoring in the Hindu Kush Himalaya; https://doi.org/10.48550/arXiv.2012.05013.
Bhambri R, Bolch T and Chaujar R K 2012 Frontal recession of Gangotri Glacier, Garhwal Himalayas, from 1965 to 2006, measured through high-resolution remote sensing data; Curr. Sci. 102(3) 489–494.
Bhatia Y 2022 Bi-modal deep neural network for gait emotion recognition; Master's thesis, University of Calgary, Calgary, Canada.
Bolibar J, Rabatel A, Gouttevin I, Galiez C, Condom T and Sauquet E 2020 Deep learning applied to glacier evolution modelling; Cryosphere 14(2) 565–584, https://doi.org/10.5194/tc-14-565-2020.
Calleja J F, Muñiz R, Fernandez S, Corbea-Pérez A, Peón J, Otero J and Navarro F 2021 Snow albedo seasonal decay and its relation with shortwave radiation, surface temperature and topography over an Antarctic ice cap; IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 14 2162–2172, https://doi.org/10.1109/JSTARS.2021.3051731.
Carey M, Molden O C, Rasmussen M B, Jackson M, Nolin A W and Mark B G 2017 Impacts of glacier recession and declining meltwater on mountain societies; Ann. Am. Assoc. Geogr. 107(2) 350–359.
Church J A, Clark P U, Cazenave A, Gregory J M, Jevrejeva S and Levermann A et al. 2013 Sea-level rise by 2100; Science 342(6165) 1445, https://doi.org/10.1126/science.342.6165.1445-a.
Das P K 2013 The Himalayan tsunami-cloudburst, flash flood and death toll: A geographical postmortem; IOSR-JESTFT 7(2) 33–45.
Dubey S and Goyal M K 2020 Glacial lake outburst flood hazard, downstream impact, and risk over the Indian Himalayas; Water Resour. Res. 56(4) e2019WR026533, https://doi.org/10.1029/2019WR026533.
Fausett L 1994 Fundamentals of neural networks; Prentice Hall, Englewood Cliffs, NJ.
Ferreira A G, Soria-Olivas E, Lopez A J S and Lopez-Baeza E 2011 Estimating net radiation at surface using artificial neural networks: A new approach; Theor. Appl. Climatol. 106 263–279, https://doi.org/10.1007/s00704-011-0488-7.
Gao S 2020 A review of recent researches and reflections on geospatial artificial intelligence; Geomat. Inf. Sci. Wuhan Univ. 45(12) 1865–1874, https://doi.org/10.13203/j.whugis20200597.
Garrard R, Kohler T, Price M F, Byers A C, Sherpa A R and Maharjan G R 2016 Land use and land cover change in Sagarmatha National Park, a world heritage site in the Himalayas of eastern Nepal; MT. Res. Dev. 36(3) 299–310, https://doi.org/10.1659/MRD-JOURNAL-D-15-00005.1.
Ghatak D, Sinsky E and Miller J 2014 Role of snow-albedo feedback in higher elevation warming over the Himalayas, Tibetan Plateau and Central Asia; Environ. Res. Lett. 9(11) 114008, https://doi.org/10.1088/1748-9326/9/11/114008.
Graves A 2013 Generating sequences with recurrent neural networks; https://doi.org/10.48550/arXiv.1308.0850.
Guidicelli M, Huss M, Gabella M and Salzmann N 2023 Spatio-temporal reconstruction of winter glacier mass balance in the Alps, Scandinavia, central Asia and western Canada (1981–2019) using climate reanalyses and machine learning; Cryosphere 17(2) 977–1002, https://doi.org/10.5194/tc-17-977-2023.
Heaton J 2008 Introduction to neural networks with Java; Heaton Research, Inc.
Hock R 2005 Glacier melt: A review of processes and their modelling; Progr. Phys. Geogr. 29(3) 362–391, https://doi.org/10.1191/0309133305pp453ra.
Ienco D, Gaetano R, Dupaquier C and Maurel P 2017 Land cover classification via multitemporal spatial data by deep recurrent neural networks; IEEE Geosci. Remote Sens. Lett. 14(10) 1685–1689, https://doi.org/10.1109/LGRS.2017.2728698.
Jain S K, Lohani A K, Singh R, Chaudhary A and Thakural L 2012 Glacial lakes and glacial lake outburst flood in a Himalayan basin using remote sensing and GIS; Nat. Hazards 62 887–899, https://doi.org/10.1007/s11069-012-0120-x.
Jankowski N and Duch W 2001 Optimal transfer function neural networks; ESANN 2001, 9th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 25–27, 2001, pp. 101–106.
Jury M W, Mendlik T, Tani S, Truhetz H, Maraun D, Immerzeel W W and Lutz A F 2020 Climate projections for glacier change modelling over the Himalayas; Int. J. Climatol. 40(3) 1738–1754, https://doi.org/10.1002/joc.6298.
Kadota T, Fujita K, Seko K, Kayastha R B and Ageta Y 1997 Monitoring and prediction of shrinkage of a small glacier in the Nepal Himalaya; Ann. Glaciol. 24 90–94, https://doi.org/10.3189/S0260305500011988.
Kamel Boulos M N, Peng G and VoPham T 2019 An overview of geoai applications in health and healthcare; Int. J. Health Geogr. 18 1–9, https://doi.org/10.1186/s12942-019-0171-2.
Karthikeyan L, Pan M, Konings A G, Piles M, Fernandez-Moran R, Kumar D N and Wood E F 2019 Simultaneous retrieval of global scale vegetation optical depth, surface roughness, and soil moisture using x-band AMSR-E observations; Rem. Sens. Environ. 234 111473, https://doi.org/10.1016/j.rse.2019.111473.
Kaushik S, Dharpure J K, Joshi P, Ramanathan A and Singh T 2020 Climate change drives glacier retreat in Bhaga Basin located in Himachal Pradesh, India; Geocarto Int. 35(11) 1179–1198, https://doi.org/10.1080/10106049.2018.1557260.
Khanal N R, Mool P K, Shrestha A B, Rasul G, Ghimire P K, Shrestha R B and Joshi S P 2015 A comprehensive approach and methods for glacial lake outburst flood risk assessment, with examples from Nepal and the transboundary area; Int. J. Water Res. Dev. 31(2) 219–237, https://doi.org/10.1080/07900627.2014.994116.
Kraaijenbrink P D, Bierkens M F, Lutz A F and Immerzeel W 2017 Impact of a global temperature rise of 1.5 degrees celsius on Asia’s glaciers; Nature 549(7671) 257–260, https://doi.org/10.1038/nature23878.
Kulkarni A V, Bahuguna I, Rathore B, Singh S, Randhawa S, Sood R and Dhar S 2007 Glacial retreat in Himalaya using Indian remote sensing satellite data; Curr. Sci. 92(1) 69–74.
Kumar V, Ranjan D and Verma K 2021a Global climate change: The loop between cause and impact; In: Global climate change, Elsevier, pp. 187–211.
Kumar V, Shukla T, Mehta M, Dobhal D, Bisht M P S and Nautiyal S 2021b Glacier changes and associated climate drivers for the last three decades, Nanda Devi region, central Himalaya, India; Quat. Int. 575 213–226, https://doi.org/10.1016/j.quaint.2020.06.017.
Kusky T M 2010 Climate change: Shifting glaciers, deserts, and climate belts; Infobase Publishing.
Lakhal M I, Çevikalp H, Escalera S and Ofli F 2018 Recurrent neural networks for remote sensing image classification; IET Comput. Vis. 12(7) 1040–1045, https://doi.org/10.1049/iet-cvi.2017.0420.
Lazaris A and Prasanna V K 2020 An LSTM framework for software-defined measurement; IEEE Trans. Netw. Serv. Manag. 18(1) 855–869, https://doi.org/10.1109/TNSM.2020.3040157.
Li X, Zhang Y, Zhang J, Chen S, Marsic I, Farneth R A and Burd R S 2017 Concurrent activity recognition with multimodal CNN-LSTM structure; https://doi.org/10.48550/arXiv.1702.01638.
Massad R S, Lathière J, Strada S, Perrin M, Personne E, Stéfanon M and de Noblet-Ducoudré N 2019 Reviews and syntheses: Influences of landscape structure and land uses on local to regional climate and air quality; Biogeosciences 16(11) 2369–2408, https://doi.org/10.5194/bg-16-2369-2019.
McCarthy J 2007 What is artificial intelligence; http://www-formal.stanford.edu/jmc/.
McCreary J 1976 Eastern tropical ocean response to changing wind systems: With application to El Niño; J. Phys. Oceanogr. 6(5) 632–645, https://doi.org/10.1175/1520-0485(1976)006<0632:ETORTC>2.0.CO;2.
Medsker L R and Jain L 2001 Recurrent neural networks; Design Appl. 5 64–67.
Mehta M, Majeed Z, Dobhal D and Srivastava P 2012 Geomorphological evidences of post-LGM glacial advancements in the Himalaya: A study from Chorabari Glacier, Garhwal Himalaya, India; J. Earth Syst. Sci. 121 149–163, https://doi.org/10.1007/s12040-012-0155-0.
Mehta M, Dobhal D, Kesarwani K, Pratap B, Kumar A and Verma A 2014 Monitoring of glacier changes and response time in Chorabari glacier, central Himalaya, Garhwal, India; Curr. Sci. 107(2) 281–289.
Mou L, Ghamisi P and Zhu X X 2017 Deep recurrent neural networks for hyperspectral image classification; IEEE Trans. Geosci. Remote Sens. 55(7) 3639–3655, https://doi.org/10.1109/TGRS.2016.2636241.
Ndikumana E, Ho Tong Minh D, Baghdadi N, Courault D and Hossard L 2018 Deep recurrent neural network for agricultural classification using multitemporal Sar Sentinel-1 for Camargue, France; Remote Sens. 10(8) 1217, https://doi.org/10.3390/rs10081217.
Patel L K, Sharma P, Fathima T and Thamban M 2018 Geospatial observations of topographical control over the glacier retreat, Miyar Basin, Western Himalaya, India; Environ. Earth Sci. 77 1–12, https://doi.org/10.1007/s12665-018-7379-5.
Pellicciotti F, Brock B, Strasser U, Burlando P, Funk M and Corripio J 2005 An enhanced temperature-index glacier melt model including the shortwave radiation balance: Development and testing for Haut Glacier D’arolla, Switzerland; J. Glaciol. 51(175) 573–587, https://doi.org/10.3189/172756505781829124.
Pellikka P and Rees W G 2009 Remote sensing of glaciers: Techniques for topographic, spatial and thematic mapping of glaciers; CRC Press.
Pomerat J, Segev A and Datta R 2019 On neural network activation functions and optimizers in relation to polynomial regression; 2019 IEEE International Conference on Big Data (big data), pp. 6183–6185.
Popescu M-C, Balas V E, Perescu-Popescu L and Mastorakis N 2009 Multilayer perceptron and neural networks; WSEAS Trans. Circuits Syst. 8(7) 579–588.
Rafiq M, Romshoo S A, Mishra A K and Jalal F 2019 Modelling Chorabari lake outburst flood, Kedarnath, India; J. Mt. Sci. 16(1) 64–76, https://doi.org/10.1007/s11629-018-4972-8.
Rahmani F, Lawson K, Ouyang W, Appling A, Oliver S and Shen C 2021 Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data; Environ. Res. Lett. 16(2) 024025, https://doi.org/10.1088/1748-9326/abd501.
Rajagopalan G 2021 Data visualization with Python libraries. A Python Data Analyst’s Toolkit: Learn Python and Python-based Libraries with applications in data analysis and statistics, pp. 243–278, https://doi.org/10.1007/978-1-4842-6399-0_7.
Ramachandran P, Zoph B and Le Q V 2017 Searching for activation functions; https://doi.org/10.48550/arXiv.1710.05941.
Ramchoun H, Ghanou Y, Ettaouil M and Janati Idrissi M A 2016 Multilayer perceptron: Architecture optimization and training; https://doi.org/10.9781/ijimai.2016.415.
Rees W G 2005 Remote sensing of snow and ice; CRC Press.
Romshoo S A and Rashid I 2010 Potential and constraints of geospatial data for precise assessment of the impacts of climate change at landscape level; Int. J. Geomat. Geosci. 1(3) 386–405.
Romshoo S A, Murtaza K O, Shah W, Ramzan T, Ameen U and Bhat M H 2022 Anthropogenic climate change drives melting of glaciers in the Himalaya; Environ. Sci. Pollut. Res. 29(35) 52,732–52,751, https://doi.org/10.1007/s11356-022-19524-0.
Saikawa E, Panday A, Kang S, Gautam R, Zusman E, Cong Z and Adhikary B 2019 Air pollution in the Hindu Kush Himalaya; In: The Hindu Kush Himalaya assessment: Mountains, climate change, sustainability and people, pp. 339–387, https://doi.org/10.1007/978-3-319-92288-1_10.
Sharifahmadian A 2015 Numerical models for submerged breakwaters: Coastal hydrodynamics and morphodynamics; Butterworth-Heinemann.
Sharma A, Liu X and Yang X 2018 Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks; Neural Netw. 105 346–355, https://doi.org/10.1016/j.neunet.2018.05.019.
Shea J, Immerzeel W, Wagnon P, Vincent C and Bajracharya S 2015 Modelling glacier change in the Everest region, Nepal Himalaya; Cryosphere 9(3) 1105–1128, https://doi.org/10.5194/tc-9-1105-2015.
Sherstinsky A 2020 Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network; Phys. D: Nonlinear Phenomena 404 132306, https://doi.org/10.1016/j.physd.2019.132306.
Singh P and Bengtsson L 2004 Hydrological sensitivity of a large Himalayan basin to climate change; Hydrol. Process. 18(13) 2363–2385, https://doi.org/10.1002/hyp.1468.
Singh R, Schickhoff U and Mal S 2016 Climate change, glacier response, and vegetation dynamics in the Himalaya; Springer International Publishing, Cham, Switzerland, https://doi.org/10.1007/978-3-319-28977-9.
Son H and Kim C 2020 A deep learning approach to forecasting monthly demand for residential-sector electricity; Sustainability 12(8) 3103, https://doi.org/10.3390/su12083103.
Sood V, Tiwari R K, Singh S, Kaur R and Parida B R 2022 Glacier boundary mapping using deep learning classification over Bara Shigri Glacier in western Himalayas; Sustainability 14(20) 13485, https://doi.org/10.3390/su142013485.
Staudemeyer R C and Morris E R 2019 Understanding LSTM – a tutorial into long short-term memory recurrent neural networks; https://doi.org/10.48550/arXiv.1909.09586.
Stephens G L, O’Brien D, Webster P J, Pilewski P, Kato S and Li J-L 2015 The albedo of Earth; Rev. Geophys. 53(1) 141–163, https://doi.org/10.1002/2014RG000449.
Stokes C R, Abram N J, Bentley M J, Edwards T L, England M H and Foppert A et al. 2022 Response of the East Antarctic ice sheet to past and future climate change; Nature 608(7922) 275–286, https://doi.org/10.1038/s41586-022-04946-0.
Taloor A J, Kothyari G C, Manhas D R, Bisht H, Mehta P, Sharma M, Mahajan S, Roy S, Singh A K and Ali S 2021 Spatio-temporal changes in the Machoi glacier Zanskar Himalaya India using geospatial technology; Quat. Sci. Adv. 4 100031, https://doi.org/10.1016/j.qsa.2021.100031.
Trenberth K E 1997 The definition of El Nino; Bull. Am. Meteorol. Soc. 78(12) 2771–2778, https://doi.org/10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2.
Tripathi J N, Sonker I, Tripathi S and Singh A K 2022 Climate change traces on Lhonak Glacier using geospatial tools; Quat. Sci. Adv. 8 100065, https://doi.org/10.1016/j.qsa.2022.100065.
Tudhope A W, Chilcott C P, McCulloch M T, Cook E R, Chappell J, Ellam R M and Shimmield G B 2001 Variability in the El Niño-southern oscillation through a glacial–interglacial cycle; Science 291(5508) 1511–1517, https://doi.org/10.1126/science.1057969.
Yue X, Li Z, Zhao J, Fan J, Takeuchi N and Wang L 2020 Variation in albedo and its relationship with surface dust at Urumqi Glacier no. 1 in Tien Shan, China; Front. Earth Sci. 8 110, https://doi.org/10.3389/feart.2020.00110.
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The authors would like to thank the Faculty of Technology, CEPT University and CEPT University management for providing the infrastructure and support to carry out this study.
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Sriram focused on data collection, data processing, development, validation of the models and drafted the manuscript. Dhwanilnath guided, supervised the analysis, model development as well as drafted and edited the manuscript. Shaily guided and supervised the analysis.
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Communicated by Chandra Prakash Dubey
This article is part of the Topical Collection: AI/ML in Earth System Sciences.
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Vemuri, S., Gautam, D. & Gandhi, S. Glacial retreat delineation using machine and deep learning: A case of a lower Himalayan region. J Earth Syst Sci 133, 82 (2024). https://doi.org/10.1007/s12040-024-02285-4
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DOI: https://doi.org/10.1007/s12040-024-02285-4