Abstract
The water level measurements in a river is important for a variety of reasons. Because of its significant impacts on various aspects, accurate water level prediction is important in river management. Three data-driven water level forecasting models are analyzed and presented. One is based on Box-Jenkins approach, while the other two are based on the ANN and SVM approaches, respectively. The analysis is made with great attention to the reliability and accuracy of each model, with reference to daily water level data of Sungai Kemaman at Jambatan Air Putih, Terengganu. The aim of this study is to propose the best model that suitable and appropriate for predicting the water level. The experimental results revealed that model SVM (2) gives the best comparative value which indicate that this model was suitable to predict the daily water level. The values of SVM (2) are MAE = 8.8453, MSE = 0.1574, RMSE = 0.3968, r = 0.8667 and CE = 0.9979. The results of this study demonstrated the suggested model’s capability in prediction of water level given the characteristics of the data which appear to be not stationary, not normally distributed and not linear. The suggested SVM (2) presents a potential alternative approach for prediction water level data, as evidenced by this research.
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Acknowledgments
The authors would like to thank the Ministry of Higher Education Malaysia (MOHE) for supporting this research under Fundamental Research Grant Scheme Vot No. FRGS/1/2018/STG06/UTHM/03/3 and partially sponsor by Universiti Tun Hussein Onn Malaysia under Multi-Displinary Grant Vot No. H508.
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Noorain, I.S., Ismail, S., Sadon, A.N., Yasin, S.M. (2022). Application of Box-Jenkins, Artificial Neural Network and Support Vector Machine Model for Water Level Prediction. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_12
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