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This study proposed a novel application of Neural Network AutoRegressive eXogenous (NNARX) model in predicting nonlinear behaviour of riverbank erosion rates which is difficult to be achieved with good accuracy using conventional approaches. This model can estimate complex river bank erosion rates with flow variations. The NNARX model analysed to a set of primary data, 60% (203 data for training) and 40% (135 data for testing), which were collected from Sg. Bernam, Malaysia. A set of nondimensional parameters, known as functional relationship, used as an input to the NNARX model has been established using the method of repeating variables. The One-Step-Ahead time series prediction plots are used to assess the accuracy of all developed models. Model no. 6 (5 independent variables with 10 hidden layers) gives good predictive performance, supported by the graphical analysis with discrepancy ratio of 94% and 90% for training and testing datasets. This finding is consistent with model accuracy result, where Model no. 6 achieved R2 of 0.932 and 0.788 for training and testing datasets, respectively. Result shows that bank erosion is maximized when the near-bank velocity between 0.2 and 0.5 m/s, and the riverbank erosion is between 1.5 and 1.8 m/year. On the other hand, higher velocities ranging from 0.8 to 1.3 m/s induces erosion at a rate between 0.1 and 0.4 m/year. Sensitivity analysis shows that the highest accuracy of 91% is given by the ratio of shear velocity to near-bank velocity followed by boundary shear stress to near-bank velocity ratio (88.5%) and critical shear stress to near-bank velocity ratio (88.2%). It is concluded that the developed model has accurately predicted non-linear behaviour of riverbank erosion rates with flow variations. The study's findings provide valuable insights in advanced simulations and predictions of channel migration, encompassing both lateral and vertical movements, the repercussions on the adjacent river corridor, assessing the extent of land degradation and in formulating plans for effective riverbank protection and management measures.

Robustness analysis of model parameters for sediment transport equation development is carried out using 256 hydraulics and sediment data from twelve Malaysian rivers. The model parameters used in the analyses include parameters in equations by Ackers-White, Brownlie, Engelund-Hansen, Graf, Molinas-Wu, Karim-Kennedy, Yang, Ariffin and Sinnakaudan. Seven parameters in five parameter classes were initially tested. Robustness of the model parameters was measured on the statistical relations through Evolutionary Polynomial Regression (EPR) technique and further examined using the discrepancy ratio of the predicted versus the measured values. Results from analyses suggest (ratio of shear velocity to flow velocity) and (ratio of hydraulic radius to mean sediment diameter) to be the most significant and influential parameters for the development of sediment transport equation.

This study aims to develop a streambank erosion prediction model using Artificial Neural Network Autoregressive Exogenous (ANNARX) for natural channels. ANNARX is one type of ANN models and it is a supervised network that trains spasmodic data sets. Field data of 494 data extracted from two (2) rivers in Selangor, namely Sg. Bernam and Sg. Lui were used in the training and testing phases. Total of eleven (11) independent variables are used as input variables in the input layer and the ratio between erosion rates, ? to the near-bank velocity, Ub as the output variable. The functional relationships were derived using Buckingham Pi Theorem in the dimensional analysis. A supervised learning technique was employed and the target output is streambank erosion rates, ?b. The established models were validated to assess their performances in predicting the rates of streambank erosion using 176 data. Validation of the newly developed streambank erosion rates equation has been conducted using data obtained from this study. The performance of the derived model was tested using discrepancy ratio and graphical analysis. Discrepancy ratio (DR) is the ratio of predicted values to the measured values and these values are deemed accurate if the data lie between 0.5 to 2.0 limit. Total of 8 models have been developed in the predictive model. Analysis confirmed that models developed using ANNARX are capable to achieve coefficient correlations (r-squared) values above 0.9 and successfully predict the measured data at accuracy above 90%.