使用INLA-SPDE与PyMC3概率编程相结合,与土壤水分含量相关的空间变化和不确定性.
1School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo, 255000, Shandong Province, China. yyjtshkh@126.com.
Scientific reports
|October 12, 2024
概括
本研究开发了一个基于数据的模型,以了解冬季小麦的土壤体积含水量 (SVMC) 的空间变化和不确定性. 使用INLA-SPDE的贝叶斯推理提供了可靠的预测,并量化了不确定性,提高了湿度预测的准确性.
科学领域:
- 农业科学 农业科学
- 地理空间分析的研究.
- 土壤科学 土壤科学
背景情况:
- 土壤体积含水量 (SVMC) 的空间变化影响预测的准确性.
- 准确的SVMC数据对于农业管理至关重要,尤其是在关键的作物生长阶段.
研究的目的:
- 开发一个数据驱动的模型来评估SVMC的空间变化和不确定性.
- 提高土壤湿度预测的准确性和可解释性.
主要方法:
- 在3公的土地上使用时间域反射计 (TDR) 采集SVMC的网格采样.
- 贝叶斯推论使用PyMC3与集成嵌套拉普拉斯近似和随机局部微分方程 (INLA-SPDE) 模型.
- 使用马尔科夫链蒙特卡洛 (MCMC) 轨迹图,内核密度估计 (KDE) 和排名图来实现模型透明度.
主要成果:
- 开发的模型准确地预测了SVMC,并使用95%可信度间隔量化了不确定性.
- 考西先验在SVMC预测中表现出比高斯先验更大的稳定性.
- 通过各种可视化技术实现了模型的透明度和可解释性.
结论:
- 使用INLA-SPDE的贝叶斯推理有效地解决了SVMC中的空间异质性和不确定性.
- 该模型为土壤湿度预测提供了一个强大的和可解释的框架.
- 通过最高后密度间隔,可以显式量化SVMC不确定性.
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