基于混合反传播神经网络模型和气象,土壤和作物数据,估计玉米的蒸发透气
Long Zhao1, Shunhao Qing1, Hui Li1
1College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China.
International journal of biometeorology
|January 10, 2024
概括
准确的玉米蒸发透气 (ET) 预测对于水资源管理至关重要. 一个新的沙猫群优化-反向传播 (SCSO-BP) 模型整合了气象,土壤和作物数据,显著提高了中国北部的ET估计准确度.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 准确的作物蒸发透气 (ET) 估计对于有效的灌和农业水资源管理至关重要.
- 现有的玉米ET模型通常依赖于气象数据和经验系数,导致不确定性.
- 开发更精确的ET预测模型对于优化农业用水使用至关重要.
研究的目的:
- 构建和优化反向传播神经网络 (BP) 模型,用于预测夏季玉米的蒸发.
- 通过整合气象,土壤和作物数据来提高BP模型的准确性.
- 评估生物优化算法的性能,包括沙猫群优化 (SCSO),猎人猎物优化器 (HPO) 和金子优化 (GJO),以改进玉米ET模型.
主要方法:
- 收集了来自中国北方的余站的夏季玉米蒸发透气数据.
- 使用气象,土壤水分和作物数据开发了一种反向传播神经网络 (BP) 模型.
- 使用沙猫群优化 (SCSO),猎人猎人优化 (HPO) 和金优化 (GJO) 算法优化了BP模型.
主要成果:
- 气象,土壤湿度和作物数据的融合有效地提高了玉米ET模型的准确性.
- 混合SCSO-BP模型在独立BP模型上表现出优异的性能,在R2,RMSE,MAE和NSE中显示出显著的改进.
- 在比较的模型中,SCSO-BP模型获得了最高的准确性,R2 = 0.842,RMSE = 0.433毫米/天,MAE = 0.316毫米/天,NSE = 0.840.
结论:
- 该SCSO-BP模型提供了一个非常准确的方法来预测每日玉米蒸发.
- 整合多种数据源和使用先进的优化算法可以提高ET模型的可靠性.
- 这些发现为中国北部和类似地区的玉米水资源管理和灌战略提供了宝贵的见解.
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