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在共享社会经济路径 (SSP) 场景下预测全球化肥料消费,采用集体机器学习的方法

Yulian Gao¹, Kecui Dong¹, Yaojie Yue¹

¹Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.

The Science of the total environment

|December 9, 2023

概括

全球肥料消费预测得到了机器学习的改进,考虑到社会经济因素. 预计N和P肥料的使用量将减少,而K肥料的使用量可能会在2100年增加.

科学领域:

农业科学农业科学
地球系统科学地球系统科学
数据科学数据科学数据科学

背景情况:

准确的全球化肥消耗预测对于地球系统模拟,化肥行业和农业科学至关重要.
以前的预测存在不确定性,原因是社会经济因素的考虑不足.

研究的目的:

通过整合社会经济因素和共享社会经济路径 (SSP) 场景,预测从2020年到2100年的全球肥料消费.
使用整体机器学习方法开发用于预测肥料消耗的可靠框架.

主要方法:

采用了组合方法,结合了六个机器学习算法.
该模型结合了社会经济因素和SSP场景来预测肥料消费.
使用纳什-萨克利夫效率,克林-古普塔效率和平均绝对百分比误差来验证模型性能.

主要成果:

根据SSP1,SSP2和SSP3情景,预计全球N和P肥料消费量将在2100年减少,百分比不同.
在所有SSP情景中,全球K肥料消费量预计将在2100年增加.
目前中国的肥料消费量最高,但N肥料热点可能会转移到拉丁美洲和加勒比地区.

结论:

整体机器学习方法为预测未来肥料消耗提供了强大而准确的方法.
该研究为了解全球肥料趋势和支持相关研究提供了有价值的数据集.
未来的肥料消费模式将在不同营养类型和地区之间有很大差异.

关键词:

动态变化动态变化整合机器学习机器学习全球肥料消费预测预测共享社会经济路径 (SSP) 场景空间模式空间模式

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