Abstract
River flow is important in hydrological or meteorological research in order to forecast flood, drought events, as well as day-to-day river basin management and it is hard to do so without both real-time and historical data. This is because of hydrology data is frequently incomplete due to a variety of factors, including data loss. Due to the hydrological data is prone to missing values, imputing the missing data is very important to complete the data. In this study, six imputation methods are used which are Mean Imputation and Median Imputation, Multiple Imputation, Normal Ratio, NIPALS and EM Algorithms. The aims of this study are to impute the missing values in river flow dataset using various imputation methods and to apply the ARIMA model on the original and imputed datasets. The experimental result showed that Multiple Imputation that used MCMC method was deemed the best method as it has the lowest value of RMSE and MAE which are 41.23 and 14.09 respectively compared to other methods. It can be concluded that Multiple Imputation is the most robust and adaptable machine learning method, but it is also the most difficult to program in terms of computing complexity.
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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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Muhaime, N.A.D.A., Arifin, M.A., Ismail, S., Shaharuddin, S.M. (2022). Comparative Performance of Various Imputation Methods for River Flow Data. 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_11
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