Full
Outline
Heat flow (HF) refers to the heat energy transmitted from the Earth's interior to the surface. 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 algorithms commonly used for regression prediction problems, namely GBDT, XGBoost, RF, LightGBM, 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 models. 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.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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