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

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 at https://github.com/vivian-ma97/ABC-Stacking-for-Subglacial-Lakes