在石灰质土壤中使用人工神经网络和多重线性回归来预测和近的液压导电性
Hasan Mozaffari1, Ali Akbar Moosavi1, Mohammad Amin Nematollahi2
1Department of Soil Science, College of Agriculture, Shiraz University, Shiraz, Iran.
PloS one
|January 10, 2024
概括
预测土壤液压导电性 (Kψ) 对水资源管理至关重要. 多层感知神经网络 (MLPNNs) 在石灰质土壤中估计Kψ时,比步骤式多重线性回归 (SMLR) 和辐射基函数神经网络 (RBFNNs) 提供更高的精度.
科学领域:
- 土壤科学 土壤科学
- 水文学的水文学
- 农业工程 农业工程
背景情况:
- 液压导电性 (Kψ) 是一个关键的土壤属性,控制水和化学物质的运动,对于灌和排水等土地管理实践至关重要.
- 准确的Kψ估计对于土壤监测和管理至关重要,但现场测量具有挑战性,结果高度变化.
- 石灰质土壤对Kψ预测具有独特的挑战,因为它们具有特定的物理化学性质.
研究的目的:
- 评估逐步多重线性回归 (SMLR),多层感知神经网络 (MLPNNs) 和辐射基函数神经网络 (RBFNNs) 的预测潜力,以估计Kψ.
- 确定最有影响力的土壤属性,以预测Kψ在不同的湿度张力 (0,5,10和15厘米) 时.
- 为了比较在石灰质土壤中Kψ预测所选的建模方法的性能.
主要方法:
- 从伊朗法尔斯省收集了102个土壤样本,包括各种土地用途的完整和复合样本.
- 使用标准实验室和现场张力盘透仪方法测量了常见的物理化学属性和液压导电性 (Kψ).
- 应用SMLR,MLPNNs和RBFNNs来预测Kψ (K0,K5,K10,K15) 使用土壤特性作为预测因素.
主要成果:
- 土壤结构参数 (例如有机物,pH,散装密度) 与Kψ在较低张力 (K5,K0) 的相关性比粒子大小分布参数更强.
- 相反,粒子大小参数在更高的电压下 (K15,K10) 与Kψ更有相关性.
- MLPNNs获得了最高的预测准确性 (R2val: 0.710.82),其次是RBFNNs (R2val: 0.580.78),以及SMLR (R2val: 0.520.63).
结论:
- 与RBFNNs和SMLR相比,MLPNNs在预测石灰质土壤中Kψ的各种张力方面表现出更好的能力.
- 虽然SMLR提供了更简单的脚转移功能,但MLPNNs提供了更准确的预测,这对于有效的土壤和水资源管理至关重要.
- 该研究强调了先进的机器学习技术在具有挑战性的土壤类型中对Kψ的可靠估计的潜力.
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