基于数据的启发式方法对地下滴灌水中的土壤含水量模拟的时间评估
Jalal Shiri1, Mohammad Hossein Kazemi2, Sepideh Karimi2
1Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran; Water Engineering and Science Research Institute (WESRI), University of Tabriz, Tabriz, Iran.
The Science of the total environment
|November 3, 2024
概括
基因表达编程准确地模拟了大米田中的土壤含水量 (SWC). 使用增长阶段进行数据分区,提高了预测准确性和稳定性,特别是对于更深层的土壤层.
科学领域:
- 农业工程 农业工程
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 准确的土壤含水量 (SWC) 估计对于农业和水资源管理至关重要.
- 现场SWC测量方法面临实际限制和复杂性.
- 启发式数据驱动模型为SWC模拟提供了一个有希望的替代方案.
研究的目的:
- 应用基因表达编程 (GEP) 来模拟在田三种深度的SWC.
- 评估不同数据分区策略 (年度与增长阶段) 对模型性能的影响.
- 评估模型准确性和不确定性,以便可靠的SWC预测.
主要方法:
- 利用基因表达编程 (GEP) 进行启发式数据驱动的SWC建模.
- 采用k倍交叉验证,使用两种时间数据分区策略:年级和成长阶段.
- 使用错误统计和不确定性分析评估模型性能.
主要成果:
- 在不同的土壤深度,GEP成功模拟了SWC.
- 基于生长阶段的分区数据与年度分区相比,产生了更准确和更稳定的SWC预测.
- 第三层土壤中的水含量得到了最高准确度的预测.
结论:
- 基因表达编程是模拟农业环境中的SWC的有效工具.
- 数据分区策略的选择显著影响模型性能和可靠性.
- 基于生长阶段的分区提高了SWC预测的准确性和稳定性,特别是对于更深层的土壤层.
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