基于神经网络优化模型的Euonymus bungeanus即时液液流速度模拟
Peng Zhou1, Lei Han2,3,4, Ling Peng1
1College of Agriculture, Ningxia University, Yinchuan 750021, China.
Ying yong sheng tai xue bao = The journal of applied ecology
|September 8, 2023
概括
预测树木木汁流对于了解森林用水量至关重要. 这项研究发现,太阳辐射,蒸汽压力赤字,空气温度和湿度显著影响液液流动,用子搜索算法优化的BP神经网络模型显示出最好的预测准确度.
科学领域:
- 生态水文学 生态水文学
- 植物生理学 植物生理学
- 计算生物学 计算生物学
背景情况:
- 准确地建模树木木汁流对于了解森林生态水文过程和区域水需求至关重要.
- 传统的多变量线性和实证模型难以捕捉液液流变化的复杂性.
- 开发一种简单可行的方法来模拟基于环境因素的液液流是非常重要的.
研究的目的:
- 为了研究环境因素对*Euonymus bungeanus*树干汁液流速的影响.
- 开发和优化一个神经网络模型,用于预测E. bungeanus的液流速.
- 确定关键的环境驱动因素及其对液液流量的定量影响.
主要方法:
- 使用热扩散液液流量计连续测量树干液流速.
- 分析液液流与包括太阳辐射,蒸汽压力赤字,空气温度和相对湿度在内的环境因素之间的关系.
- 应用粒子群集优化 (PSO) 和子搜索算法 (SSA) 来优化反向传播 (BP),Elman和极端学习机器 (ELM) 神经网络模型用于液流预测.
主要成果:
- 太阳辐射,蒸汽压力赤字,空气温度和相对湿度被确定为影响液液流动的主要环境因素,其重要性值分别为32.5%,25.3%,22.0%和16.1%.
- 观察到液液流与环境因素之间存在hysteresis循环关系.
- 与其他优化模型相比,子搜索算法优化的BP神经网络 (SSA-BP) 显示出优异的预测性能,显著提高了综合评估指数 (GPI).
结论:
- 用子搜索算法优化的BP神经网络模型提供了一个最佳的方法来预测*Euonymus bungeanus*的瞬间汁液流速.
- 了解液液流与环境因素之间的定量关系对于准确的生态水文建模至关重要.
- 该研究强调了先进的优化算法的有效性,提高了神经网络模型对植物生理过程的预测准确度.
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