Assessment of infiltration models developed using soft computing techniques
Assessment of infiltration models developed using soft computing techniques
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使用软计算技术开发的渗透模型的评估
DOI：
10.1080/24749508.2020.1720475
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Balraj Singh
中科院分区：
文献类型：
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作者：
Parveen Sihag;Munish Kumar;Balraj Singh
摘要在这项研究中，支持向量机（SVM），高斯过程（GP），人工神经网络（ANN），和随机森林（RF）为基础的回归方法的预测能力进行了测试的土壤样品具有不同成分的砂，粉砂，粘土，粉煤灰的入渗数据。此外，他们的表现进行了比较，与Kostiakov模型（KM）和菲利普的模型（PM）。从不同土壤样品的土壤入渗速率的实验测量中收集了总共包含392个观测值的数据集。在总数据集中，随机选择272个记录进行训练，并选择剩余的120个观察结果用于验证开发的模型。标准的统计参数被用来衡量各种开发的模型的预测能力。结果表明，基于多项式核函数的GP回归（GP_Poly）可以实现最佳性能，相关系数值为0.9824，0.9863，偏差值为0.0006，-2.3542，均方根误差值为47.7336，40.3026，Nash Sutcliffe模型效率值为0.9655，0.9727，分别使用训练和测试数据集。此外，时间被发现是最有影响力的输入变量预测入渗率时，基于GP_Poly-based模型来预测入渗率。
ABSTRACT In this study, predicting ability of support vector machines (SVM), Gaussian process (GP), artificial neural network (ANN), and Random forests (RF) based regression approaches was tested on the infiltration data of soil samples having different compositions of sand, silt, clay, and fly ash. In addition to this, their performances were compared with the Kostiakov model (KM) and Philip’s model (PM). Dataset containing a total of 392 observations was collected from the experimental measurements of soil infiltration rate on different soil samples. Out of the total dataset, 272 recordings were randomly selected for training and the residual 120 observations were selected for validation of the developed models. Standard statistical parameters were used to measure the predicting ability of various developed models. The result suggests that the best performance could be achieved by Polynomial kernel function-based GP regression (GP_Poly) with coefficient of correlation values as 0.9824, 0.9863, Bias values as 0.0006, −2.3542, root-mean-square error values as 47.7336, 40.3026, and Nash Sutcliffe model efficiency values as 0.9655, 0.9727 using training and testing dataset, respectively. Furthermore, time is found as the most influencing input variable for predicting the infiltration rate when GP_Poly-based model is used to predict the infiltration rate.