基于集成学习Stacking算法的南极热流预测模型OA

Heat flow(HF)refers to the heat energy transmitted from the Earth's interior to the sur-face.It can reveal various processes occurring in the deep Earth and information about energy balance.In the Antarctic region,understanding heat flow is of great significance for simulating the dynamic changes of ice sheets.This study employs the Stacking algorithm in machine learning to construct a heat flow prediction model for Antarctica.The model integrates 13 types of geological and geophysical features related to heat flow as observational input data and incorporates six machine learning algo-rithms commonly used for regression prediction problems,namely GBDT,XGBoost,RF,LightG-BM,ET,and MLP,to predict the distribution characteristics of heat flow.The experimental results show that the prediction accuracy of the Stacking model is superior to that of several benchmark mod-els.The new Antarctic heat flow distribution prediction map obtained through this model is more in line with the actual distribution of heat flow in Antarctica compared with the large-scale estimated heat flow distribution maps drawn by traditional methods,demonstrating more excellent performance.