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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


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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  1. Aliev RA, Guirimov BG (2014) Type-2 fuzzy neural networks and their applications. Springer

  2. Aljanabi QA, Chik Z, Allawi MF, El-Shafie AH, Ahmed AN, El-Shafie A (2018) Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment. Neural Comput Appl 30(8):2459–2469.

    Article  Google Scholar 

  3. Al-Mukhtar M (2019) Random forest, support vector machine, and neural networks to modelling suspended sediment in Tigris River-Baghdad. Environ Monit Assess191(11).

  4. Anderson (1995) An introduction to neural networks

  5. Buyukyildiz M, Kumcu SY (2017) An estimation of the suspended sediment load using adaptive network based fuzzy inference system, support vector machine and artificial neural network models. Water Resour Manag 31(4):1343–1359

    Article  Google Scholar 

  6. Chen T (2016) “XGBoost : A Scalable Tree Boosting System,”

  7. Chen XY, Chau KW (2016) “A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation”.

  8. Cigizoglu HK (2004) Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Adv Water Resour 27(2):185–195.

    Article  Google Scholar 

  9. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303–314.

    Article  Google Scholar 

  10. Ehteram M et al (2021) Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction. Environ Sci Pollut Res 28(2):1596–1611.

    Article  Google Scholar 

  11. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Statist 29(5):1189–1232

    Article  Google Scholar 

  12. Gajbhiye S, Mishra SK, Pandey A (2015) Simplified sediment yield index model incorporating parameter curve number. Arab J Geosci 8(4):1993–2004

    Article  Google Scholar 

  13. Gajbhiye S, Ashish SKM (2014) Relationship between SCS-CN and sediment yield.

  14. Himanshu SK, Pandey A, Yadav B (2017) Ensemble wavelet-support vector machine approach for prediction of suspended sediment load using hydrometeorological data. J Hydrol Eng 22(7):5017006

    Article  Google Scholar 

  15. Jain SK (2001) Development of integrated sediment rating curves using ANNS. J Hydraul Eng 127(1):30–37

    Article  Google Scholar 

  16. Kisi O (2012) Modeling discharge-suspended sediment relationship using least square support vector machine. J Hydrol 456–457:110–120.

    Article  Google Scholar 

  17. Kisi O, Yaseen ZM (2019) The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction. CATENA 174:11–23

    Article  Google Scholar 

  18. Lin J, Cheng C, Chau K (2010) Using support vector machines for long-term discharge prediction. Hydrol Sci J 51:599–612.

    Article  Google Scholar 

  19. Melesse AM, Ahmad S, Mcclain ME, Wang X, Lim YH (2011) Suspended sediment load prediction of river systems: An artificial neural network approach. Agric Water Manag 98(5):855–866.

    Article  Google Scholar 

  20. Noori R et al (2011) Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. J Hydrol 401(3–4):177–189.

    Article  Google Scholar 

  21. Nourani V, Andalib G (2015) Daily and monthly suspended sediment load predictions using wavelet based artificial intelligence approaches. J Mt Sci 12(1):85–100.

    Article  Google Scholar 

  22. Nourani V, Gokcekus H, Gelete G (2021) Estimation of suspended sediment load using artificial intelligence-based ensemble model. Complexity 2021.

  23. Olyaie E, Banejad H, Chau K (2015) A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems : a case study in United States.

  24. Pham BT, Shirzadi A, Tien Bui D, Prakash I, Dholakia MB (2018) A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: a case study in the Himalayan area, India. Int J Sediment Res 33(2):157–170.

  25. Practical Neural Network Recipes in C++ - Timothy Masters - Google Books. Accessed 18 Feb 2020

  26. Rashidi S, Vafakhah M, Lafdani EK, Javadi MR (2016) Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arab J Geosci 9(11).

  27. Salih ZM, Sharafati SQ, Khosravi A, Faris K, Kisi F, Tao O, Ali H, Yaseen M (2019) River suspended sediment load prediction based on river discharge information: application of newly developed data mining models. Hydrol Sci J.

  28. Samadianfard S, Hashemi S, Kargar K, Izadyar M (2020) Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm. Energy Rep 6:1147–1159.

    Article  Google Scholar 

  29. Shadkani S, Abbaspour A, Samadianfard S, Hashemi S, Mosavi A, Band SS (2021) Comparative study of multilayer perceptron-stochastic gradient descent and gradient boosted trees for predicting daily suspended sediment load: the case study of the Mississippi River, U.S. Int J Sediment Res 36(4):512–523.

  30. Shahin MA, Maier HR, Jaksa MB (2002) Predicting Settlement of Shallow Foundations using Neural Networks. J Geotech Geoenvironmental Eng 128(9):785–793.

    Article  Google Scholar 

  31. Sharafati A, Haji Seyed Asadollah SB, Motta D, Yaseen ZM (2018) Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis. Hydrol Sci J 0(0):1–21.

  32. Singh HV, Thompson AM, Gharabaghi B (2016) Event runoff and sediment-yield neural network models for assessment and design of management practices for small agricultural watersheds. J Hydrol Eng 1–12.

  33. Tan ML (2014) Free internet datasets for streamflow modelling using SWAT in the Johor river basin, Malaysia. IOP Conf Ser Earth Environ Sci 18(1).

  34. Tan ML, Ibrahim AL, Yusop Z, Duan Z, Ling L (2015) Impacts de l’utilisation des sols et de la variabilité climatique sur les composantes hydrologiques dans le bassin du fleuve Johor, en Malaisie. Hydrol Sci J 60(5):873–889.

    Article  Google Scholar 

  35. Tangang FT, Juneng L, Salimun E, Sei K, Halimatun M (2012) Climate change and variability over Malaysia: gaps in science and research information. Sains Malaysiana 41(11):1355–1366

    Google Scholar 

  36. Targhi AT, Abbaszadeh S, Arabasadi Z (2017) A hybrid method for forecasting river suspended sediments in Iran. Int J River Basin Manag 0:1–26.

    Article  Google Scholar 

  37. Taşar B, Kaya YZ, Varçin H, Üneş F, Demirci M (2017) Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach. Int J Adv Eng Res Sci 4(12):79–84.

    Article  Google Scholar 

  38. Wu C (2020) “A hybrid model coupled with singular spectrum analysis for daily rainfall prediction A hybrid model coupled with singular spectrum analysis for daily rainfall prediction,” no. April.

  39. Yoon H, Jun S, Hyun Y, Bae G, Lee K (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coast. J Hydrol 396(1–2):128–138.

    Article  Google Scholar 

  40. Ziyan Z (2012) “Early Flood Warning for Linyi Watershed by the GRAPES / XXT Model Using TIGGE Data,” no. 973, pp. 103–111.

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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).

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