In the design of permeable stormwater channels, the ability to quantify infiltration rates accurately is important for assessing the capability of such channels to perform their required functions. Most of the available infiltration models neglect the effects of water level and channel section on the infiltration rate. In this study, physical channel models, with different channel sections, were developed in the laboratory and used to measure the infiltration rates. The performance of three soft computing techniques, including Gaussian process regression, M5P, and random forest (RF) models, were evaluated against measured values. Seven independent input variables, namely, channel side slope (m), base width (b), water level (y), sand (%), silt (%), clay (%), and time (T) and the output variable infiltration rate (f(t)), were considered in the model development and validation. The Gaussian progression–Pearson VII universal kernel function model approach was found to perform best for the data set considered, followed by the RF‐based model. The sensitivity investigation showed that time, water level, and channel side slope were the most influential input variables in predicting infiltration rates for permeable stormwater channels and should be given primary consideration in designing such channels. © 2020 John Wiley & Sons, Ltd.

中文翻译：

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使用软计算技术模拟渗透性雨水通道中的入渗速率*

在可渗透雨水通道的设计中，准确量化渗透率的能力对于评估此类通道执行其所需功能的能力很重要。大多数可用的渗透模型都忽略了水位和通道截面对渗透率的影响。在这项研究中，在实验室中开发了具有不同通道截面的物理通道模型，并将其用于测量渗透率。针对测量值评估了三种软计算技术的性能，包括高斯过程回归，M5P和随机森林（RF）模型。七个独立的输入变量，即通道边坡度（m），基准宽度（b），水位（y），沙（％），粉砂（％），黏土（％）和时间（T）以及输出变量渗透率（f（t））在模型开发和验证中均已考虑在内。发现高斯级数-皮尔逊VII通用核函数模型方法对于所考虑的数据集表现最佳，其次是基于RF的模型。敏感性调查表明，时间，水位和河道边坡是预测渗透性雨水河道入渗率最有影响的输入变量，在设计此类河道时应首先考虑这些因素。分级为4 +©2020 John Wiley＆Sons，Ltd.