气候变化领域集成服务门户(CCPortal): Deep learning applied to glacier evolution modelling

CCPortal

DOI	10.5194/tc-14-565-2020
	Deep learning applied to glacier evolution modelling
	Bolibar J.; Rabatel A.; Gouttevin I.; Galiez C.; Condom T.; Sauquet E.
发表日期	2020
ISSN	19940416
起始页码	565
结束页码	584
卷号	14 期号:2
英文摘要	We present a novel approach to simulate and reconstruct annual glacier-wide surface mass balance (SMB) series based on a deep artificial neural network (ANN; i.e. deep learning). This method has been included as the SMB component of an open-source regional glacier evolution model. While most glacier models tend to incorporate more and more physical processes, here we take an alternative approach by creating a parameterized model based on data science. Annual glacier-wide SMBs can be simulated from topo-climatic predictors using either deep learning or Lasso (least absolute shrinkage and selection operator; regularized multilinear regression), whereas the glacier geometry is updated using a glacier-specific parameterization. We compare and cross-validate our nonlinear deep learning SMB model against other standard linear statistical methods on a dataset of 32 French Alpine glaciers. Deep learning is found to outperform linear methods, with improved explained variance (up to C 64 % in space and C108 % in time) and accuracy (up to C47 % in space and C58 % in time), resulting in an estimated r2 of 0.77 and a root-mean-square error (RMSE) of 0.51 m w.e. Substantial nonlinear structures are captured by deep learning, with around 35 % of nonlinear behaviour in the temporal dimension. For the glacier geometry evolution, the main uncertainties come from the ice thickness data used to initialize the model. These results should encourage the use of deep learning in glacier modelling as a powerful nonlinear tool, capable of capturing the nonlinearities of the climate and glacier systems, that can serve to reconstruct or simulate SMB time series for individual glaciers in a whole region for past and future climates. © 2020 Author(s).
英文关键词	artificial neural network; geometry; glacier flow; glacier mass balance; ice thickness; learning; parameterization; shrinkage; time series analysis
语种	英语
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/202031
作者单位	Univ. Grenoble Alpes, CNRS, IRD, G-INP, Institut des Géosciences de l'Environnement (IGE, UMR 5001), Grenoble, France; INRAE, UR RiverLy, Villeurbanne, Lyon, France; Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France; Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France
推荐引用方式 GB/T 7714	Bolibar J.,Rabatel A.,Gouttevin I.,et al. Deep learning applied to glacier evolution modelling[J],2020,14(2).
APA	Bolibar J.,Rabatel A.,Gouttevin I.,Galiez C.,Condom T.,&Sauquet E..(2020).Deep learning applied to glacier evolution modelling.Cryosphere,14(2).
MLA	Bolibar J.,et al."Deep learning applied to glacier evolution modelling".Cryosphere 14.2(2020).