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Models for Predicting River Suspended Sediment Load Using Machine Learning: A Survey

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  • Lubna Jamal Chachan

Abstract

Suspended sediment load (SSL) prediction study is critical to water resource management. This paper presents studies related to the prediction of SSL using machine learning (ML) algorithms over the last 13 years. This research gives a survey of current studies that are used machine learning techniques to predict sediment load on several rivers in different reign. Also, it aims to find a performance model to predict the SSL. This is done by making comparisons between several studies that used machine learning techniques to predict sediment load on several rivers using different time scales. Several metrics were used to determine the best prediction model. Most of the metrics used are: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-Squared (R2) and Nash-Sutcliffe Efficiency Coefficient (NSE). The results of comparisons using different ML algorithms to predict the SSL have shown that the Multilayer perceptron (MLP) algorithm is the best compared to other algorithms.

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  • Lubna Jamal Chachan, 2022. "Models for Predicting River Suspended Sediment Load Using Machine Learning: A Survey," Technium, Technium Science, vol. 4(1), pages 239-249.
    • Handle: RePEc:tec:techni:v:4:y:2022:i:1:p:239-249
    DOI: 10.47577/technium.v4i10.8099
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    1. Melesse, A.M. & Ahmad, S. & McClain, M.E. & Wang, X. & Lim, Y.H., 2011. "Suspended sediment load prediction of river systems: An artificial neural network approach," Agricultural Water Management, Elsevier, vol. 98(5), pages 855-866, March.
    2. Sarita Gajbhiye Meshram & M. A. Ghorbani & Ravinesh C. Deo & Mahsa Hasanpour Kashani & Chandrashekhar Meshram & Vahid Karimi, 2019. "New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2335-2356, May.
    3. Wing Son Loh & Ren Jie Chin & Lloyd Ling & Sai Hin Lai & Eugene Zhen Xiang Soo, 2021. "Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments," Mathematics, MDPI, vol. 9(23), pages 1-18, December.
    4. Siyamak Doroudi & Ahmad Sharafati & Seyed Hossein Mohajeri & Haitham Afan, 2021. "Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method," Complexity, Hindawi, vol. 2021, pages 1-13, March.
    5. Sarita Gajbhiye Meshram & Vijay P. Singh & Ozgur Kisi & Vahid Karimi & Chandrashekhar Meshram, 2020. "Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4561-4575, December.
    6. Meral Buyukyildiz & Serife Yurdagul Kumcu, 2017. "An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1343-1359, March.
    7. Vahid Nourani & Huseyin Gokcekus & Gebre Gelete & Haitham Afan, 2021. "Estimation of Suspended Sediment Load Using Artificial Intelligence-Based Ensemble Model," Complexity, Hindawi, vol. 2021, pages 1-19, February.
    8. Ashish Kumar & Pravendra Kumar & Vijay Kumar Singh, 2019. "Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1217-1231, February.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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