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机器学习模型用于中国不同气候区的每日净辐射预测

Haiying Yu¹, Shouzheng Jiang², Minzhi Chen³

¹College of Engineering, Sichuan Normal University, Chengdu, 610066, China.

Scientific reports

|September 3, 2024

概括

极端学习机器 (ELM) 和带有遗传算法 (GANN) 的混合人工神经网络模型准确地估计了中国的每日净辐射 (R_n). 这些模型在精准农业中优于随机森林 (RF) 和通用回归神经网络 (GRNN).

科学领域:

农业气象农业气象学
土地表面能量平衡土地表面能量平衡
机器学习应用机器学习应用

背景情况:

净辐射 (R_n) 对陆地表面的能量循环和精密农业至关重要.
准确估计R_n对于优化作物管理策略至关重要.

研究的目的:

评估四个机器学习模型在中国不同气候区的每日R_n估计中的性能.
为了比较极端学习机器 (ELM),遗传算法神经网络 (GANN),通用回归神经网络 (GRNN) 和随机森林 (RF) 模型的有效性.

主要方法:

使用气象数据 (温度,湿度,阳光,太阳辐射) 作为输入.
使用统计指标评估模型性能:R2,RMSE,MAE和纳什-萨克利夫系数 (NS).
在中国四个不同的气候区进行了ELM,GANN,GRNN和RF模型的比较.

主要成果:

所有模型都显示R_n的轻微低估,线性回归斜率在0.810-0.870.87之间.
ELM和GANN模型表现出卓越的性能,R值高达0.963,优于RF和GRNN.
与RF和GRNN相比,ELM和GANN的模拟误差 (RMSE,MAE,NS) 较低.

结论:

ELM和GANN模型对于估计中国不同气候区的每日净辐射非常有效.
由于其更快的计算速度,特别推ELM,为精密农业提供了实用解决方案.
机器学习模型提供可靠的R_n估计,与观察到的数据分布可比.

关键词:

人工智能的人工智能是人工智能.清洁能源是一种清洁的能源.气候区气候区的气候区.模型比较模型比较净辐射辐射总量是多少

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