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贝叶斯信念网络在使用高频水化学和土地管理数据的多种农业水域中可转移

Camilla Negri¹, Nicholas Schurch², Andrew J Wade³

¹Agricultural Catchments Programme, Teagasc Environment Research Centre, Johnstown Castle, Co. Wexford Y35 Y521, Ireland; The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH, UK; University of Reading, Department of Geography and Environmental Science, Whiteknights, Reading RG6 6DR, UK; Biomathematics and Statistics Scotland, Craigiebuckler, Aberdeen AB15 8QH, UK.

The Science of the total environment

|July 26, 2024

概括

这项研究测试了贝叶斯信念网络 (BBN) 模型,用于预测不同流域的河流中的 (P). 改进有助于提高其准确性和可用于更广泛的环境应用的通用性.

科学领域:

环境科学环境科学
水文学的水文学
生物地质化学生物地质化学

背景情况:

生物地质化学水域模型往往缺乏超出其开发地点的可转移性.
评估模型可转移性对于提高预测能力和应用范围至关重要.

研究的目的:

评估贝叶斯信念网络 (BBN) 模型对流 (P) 度的可转移性.
为了提高模型的预测能力和在各种采集区的概括性.

主要方法:

将以前开发的BBN应用于具有不同水文和土地利用特征的三个新水域.
修改模型结构,包括内流P去除,地下水P输入和污水处理效应.
测量流量,度和土壤P;比较预测与观察到的总反应性P (TRP) 度.

主要成果:

原来的BBN在排水不良和适度的水域表现良好,但在地下水占主导地位的水域表现不佳.
模型改进 (地下水P,污水处理,内流吸收) 显著提高了性能 (PBIAS -5%至18%).
灵敏度分析确定了冗余变量,简化了模型.

结论:

改进的BBN显示了更好的可转移性和通用性,用于预测流P度.
模型修改有效地捕捉了各种采集区设置中的关键生物地质化学过程.
精简的BBN适用于环境管理和研究中的更广泛应用.

关键词:

专家引发的诱导方式混合网络是混合网络.模型的普遍性模型的普遍性是一种的物质.灵敏度分析是一种灵敏度分析.结构不确定性结构不确定性

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