Work

Automated Prediction of Gamburtsev Subglacial Lakes in East Antarctica With Optimized Stacking Ensemble Learning

Year: 2025

Type: article

Abstract: The development in machine learning (ML) technology has brought new horizons for the prediction of subglacial lakes (SLs) using radio-echo sounding (RES) data, offering fresh perspectives toward the automated identification of SLs. Nonetheless, the inherent data imbalance across various classes within the dataset presents significant analytical challenges. To address this limitation, the artificial bee colony (ABC) optimization algorithm is introduced to automatically predict SLs in Gamburtsev Province in East Antarctica, using an optimized stacking ensemble learning approach. The proposed method predicts SLs by using five representative features selected through importance and correlation analyses of eight features derived from RES data. The experimental outcomes demonstrate the superiority of this method in overcoming the significant imbalance of RES data, successfully identifying known lakes in the validation dataset. Furthermore, this study summarizes an inventory of SLs across the Gamburtsev subglacial mountains in East Antarctica, and a total of 55 new candidate SLs with lengths ranging from 108 to 38130 m have been predicted using our novel method. The source code is publicly available athttps://github.com/vivian-ma97/ABC-Stacking-for-Subglacial-Lakes

Source: IEEE Transactions on Geoscience and Remote Sensing

DOI: 10.1109/tgrs.2025.3587133

Authors: Qian Ma, Tong Hao, Tiantian Feng, Gang Qiao, Asoke K. Nandi, Chen Lv

Institutions: Tongji University, Brunel University of London

Language: English

Topic: Cryospheric studies and observations

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

Funders: National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities