极端学习机器用于土壤温度预测,仅使用空气温度作为输入
1National Institute of Agronomic Research of Algeria - Institut National de la Recherche Agronomique d'Algérie (INRAA), 2 route des frères Ouaddek, El Harrach, 16200, Algiers, Algeria. majenaissa@yahoo.fr.
Environmental monitoring and assessment
|July 16, 2023
概括
极端学习机器 (ELM) 使用最小的气候数据准确预测土壤温度 (ST). 包括一年中的一天 (DOY) 显著改善了农业应用的模型性能.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 机器学习 机器学习
背景情况:
- 土壤温度 (ST) 对农业至关重要,但在阿尔及利亚等发展中国家数据很少.
- 由于天气数据有限,准确的ST预测具有挑战性.
研究的目的:
- 调查极端学习机器 (ELM) 在各种深度 (30,60,100厘米) 的每日ST预测中的有效性.
- 使用ELM识别最佳气候输入变量,以准确地预测ST.
- 将ELM性能与传统建模技术进行比较.
主要方法:
- 开发了ELM模型,使用最大/最小空气温度,相对湿度和年度日 (DOY) 的组合.
- 评估了五个输入变量集,以确定最有效的组合.
- 将ELM预测与多重线性回归,人工神经网络和自适应神经模糊推理系统进行了比较.
主要成果:
- 使用空气温度和DOY (ELM-M0,ELM-M3) 的ELM模型在所有深度都超过了其他方法.
- 在100厘米深处,ELM-M3模型 (最大/最小空气温度,DOY) 实现了最高的精度 (R=0.98,RMSE=0.68°C,MAPE=3.4%).
- 加入DOY显著提高了ST预测模型的性能和准确性.
结论:
- ELM是一种快速,简单和有效的工具,用于精确地预测土壤温度.
- 该研究强调了将DOY等时间数据纳入研究的重要性,以提高机器学习模型的准确性.
- 这些发现为数据稀缺的地区提供了有价值的解决方案,需要可靠的土壤温度预测.
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