在半干旱的大陆气候中,使用基于计算智能范式的气象预测器开发了可靠的每日土壤温度估计
Meysam Alizamir1, Kaywan Othman Ahmed2, Sungwon Kim3
1Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran.
PloS one
|December 27, 2023
概括
这项研究开发了人工智能模型,使用气象数据估计各种深度的每日土壤温度. 高斯过程回归 (GPR) 通常显示出最佳性能,提供准确的土壤温度预测.
科学领域:
- 环境科学 环境科学
- 土壤科学 土壤科学
- 环境监测中的人工智能
背景情况:
- 土壤温度 (ST) 显著影响土壤的生物和化学过程,包括微生物活动和有机物分解.
- 植物生长和营养的可用性直接和间接地受到土壤温度变化的影响.
- 准确的ST估计对于土壤研究至关重要,需要使用气象参数的工具.
研究的目的:
- 开发和评估基于人工智能的模型,用于在多个深度 (05-100cm) 每天估计土壤温度.
- 评估多层感知器人工神经网络 (MLPANN),支持向量回归 (SVR),随机森林回归 (RFR) 和高斯过程回归 (GPR) 模型的有效性.
- 在两个不同的气象站使用统计指标比较模型性能,如RMSE,MAE和NS效率.
主要方法:
- 利用气象数据,包括最大/最小空气温度,相对湿度,降水和风速作为输入参数.
- 实施并比较MLPANN,SVR,RFR和GPR模型用于日常土壤温度预测.
- 在苏莱曼尼和杜坎站使用相关系数 (r),根平均平方误差 (RMSE),纳什-萨特克利夫 (NS) 效率和平均绝对误差 (MAE) 评估模型性能.
主要成果:
- 高斯过程回归 (GPR) 在预测05,10,20和100厘米深度的土壤温度方面表现出卓越的性能.
- 在苏莱曼尼站,MLPANN在50厘米深处取得了最佳结果,而在杜坎站,SVR在05厘米深处表现最好.
- 在10厘米,30厘米和50厘米深度,GPR也取得了最佳结果,这表明它在各种深度和位置具有强大的预测能力.
结论:
- 开发的人工智能模型,特别是GPR,是有效的工具,用于在不同深度每天估计土壤温度.
- 这项研究证实了这些模型在土壤科学和环境监测方面的实际应用潜力.
- 气象参数可以可靠地作为准确的土壤温度预测模型的输入.
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