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A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia

Abstract

Accurate and reliable suspended sediment load (SSL) prediction models are necessary for the planning and management of water resource structures. In this study, four machine learning techniques, namely Gradient boost regression (GBT), Random Forest (RF), Support vector machine (SVM), and Artificial neural network ANN will be developed to predict SSL at the Rantau Panjang station on Johor River basin (JRB), Malaysia. Four evaluation criteria, including the Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash Sutcliffe Efficiency (NSE) and Scatter Index (SI) will utilize to evaluating the performance of the proposed models. The obtained results revealed that all the proposed Machine Learning (ML) models showed superior prediction daily SSL performance. The comparative outcomes among models were carried out using the Taylor diagram. ANN model shows more reliable results than other models with R of 0.989, SI of 0.199, RMSE of 0.011053 and NSE of 0.979. A sensitivity analysis of the models to the input variables revealed that the absence of current day Suspended sediment load data SSLt-1 had the most effect on the SSL. Moreover, to examine validation of most accurate model we proposed divided data to 50% training, 25% testing and 25% validation) sets and ANN provided superior performance. Therefore, the proposed ANN approach is recommended as the most accurate model for SSL prediction.

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Acknowledgements

This research was supported by the Ministry of Education (MOE) through Fundamental Research Grant Scheme (FRGS/1/2020/TK0/UNITEN/02/16). The authors would like to acknowledge the access of data from Department of Irrigation and Drainage Malaysia (JPS).

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Correspondence to Ali Najah Ahmed.

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Hanoon, M.S., Abdullatif B, A.A., Ahmed, A.N. et al. A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia. Earth Sci Inform (2021). https://doi.org/10.1007/s12145-021-00689-0

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Keywords

  • Machine learning (ML)
  • ANN
  • SVM
  • GBT
  • RF
  • Prediction
  • Suspended sediment load (SSL)