MATEC Web Conf.
Volume 255, 2019Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
|Number of page(s)||7|
|Section||Health Monitoring and Diagnosis|
|Published online||16 January 2019|
Streambank Erosion Prediction for Natural Channel using Artificial Neural Network Autoregressive Exogenous (ANNARX) Model
1 I-GEO Disaster Research Centre, Infrastructure University Kuala Lumpur (IUKL), Jalan Ikram-Uniten, 43000 Kajang, Selangor, Malaysia
2 Faculty of Civil Engineering, Universiti Teknologi Mara, Shah Alam, Selangor, Malaysia
* Corresponding author: firstname.lastname@example.org
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%.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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