Abstract

This study presents a novel and comprehensive model development technique to predict the riverbank erosion rate for a natural channel using a Nonlinear AutoRegressive model with eXogenous inputs and QR factorization parameter estimation, known as the NARX-QR Factorization model. The model was developed based on a 12-month extensive field measurement at Sg. Bernam. This study established the governing factors and derived dependent and independent variables for riverbank erosion using dimensional analysis, based on the Buckingham PI theorem. Two functional relationships were derived from dimensional analysis incorporating the factors governing riverbank erosion. The functional relationships include parameters of hydraulic characteristics of the channel, riverbank geometry and soil characteristics. Parameter estimation was conducted using a linear least squares technique to quantify riverbank erosion rates. The significant independent variables and fourteen models with several numbers of hidden layers were set as the input parameters to the NARX-QR Factorization model. The model performance analysis shows that Models 1 and 9, developed based on the proposed NARX-QR Factorization model, have the highest R² at 75% and 91%, respectively. Model 1 performed the best with accuracies for training and testing datasets of 75% and 73%, respectively. Additionally, the scatter plot of Model 1 is uniformly distributed along the line of perfect agreement. Therefore, it is concluded that the NARX-QR Factorization model developed in this study performed well in estimating the riverbank erosion rate, particularly for a natural river similar to Sg. Bernam.

中文翻译：

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利用NARX-QR因子分解模型开发自然河道河岸侵蚀速率预测器：以Sg为例。马来西亚雪兰莪伯南

摘要

这项研究提出了一种新颖而全面的模型开发技术，该模型可以使用带有外源输入和QR分解参数估计的非线性自回归模型（称为NARX-QR分解模型）来预测自然河道的河岸侵蚀速率。该模型是根据在Sg进行的12个月的广泛现场测量而开发的。伯南 本研究基于白金汉PI定理，通过尺寸分析，确定了河岸侵蚀的控制因素并推导出因变量和自变量。从尺寸分析中得出了两个功能关系，其中包含了控制河岸侵蚀的因素。功能关系包括河道水力特征，河岸几何形状和土壤特征的参数。使用线性最小二乘技术进行参数估计以量化河岸侵蚀率。将重要的自变量和具有多个隐藏层的14个模型设置为NARX-QR分解模型的输入参数。模型性能分析表明，基于建议的NARX-QR分解模型开发的模型1和9具有最高的R ^2分别为75％和91％。模型1的表现最佳，其训练和测试数据集的准确性分别为75％和73％。此外，模型1的散布图沿完美一致线均匀分布。因此，可以得出结论，本研究开发的NARX-QR因子分解模型在估算河岸侵蚀率方面表现良好，尤其是对于与Sg相似的天然河流。伯南