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Estimation of Suspended Sediment Load Using Artificial Intelligence-Based Ensemble Model

Author

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  • Vahid Nourani
  • Huseyin Gokcekus
  • Gebre Gelete
  • Haitham Afan

Abstract

Suspended sediment modeling is an important subject for decision-makers at the catchment level. Accurate and reliable modeling of suspended sediment load (SSL) is important for planning, managing, and designing of water resource structures and river systems. The objective of this study was to develop artificial intelligence- (AI-) based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI-based models, that is, support vector machine (SVM), adaptive neurofuzzy inference system (ANFIS), feed-forward neural network (FFNN), and one conventional multilinear regression (MLR) modes, were used for SSL modeling. Besides, four different ensemble methods, neural network ensemble (NNE), ANFIS ensemble (AE), weighted average ensemble (WAE), and simple average ensemble (SAE), were developed by combining the outputs of the four single models to improve their predictive performance. The study used two-year (2016-2017) discharge and SSL data for training and verification of the applied models. Determination coefficient (DC) and root mean square error (RMSE) were used to evaluate the performances of the developed models. Based on the performance measure results, the ANFIS model provides higher efficiency than the other developed single models. Out of all developed ensemble models, the nonlinear ANFIS model combination method was found to be the most accurate method and could increase the efficiency of SVM, MLR, ANFIS, and FFNN models by 19.02%, 37%, 9.73%, and 16.3%, respectively, at the verification stage. Overall, the proposed ensemble models in general and the AI-based ensemble in particular provide excellent performance in SSL estimation.

Suggested Citation

  • 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.
  • Handle: RePEc:hin:complx:6633760
    DOI: 10.1155/2021/6633760
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    Cited by:

    1. 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.
    2. Seoro Lee & Jonggun Kim & Gwanjae Lee & Jiyeong Hong & Joo Hyun Bae & Kyoung Jae Lim, 2021. "Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    3. AL Jaied Chowdhury & Suheda Akter Riya, 2022. "Development of a Decision Support System (DSS) Model Predicating on the procedures of Simple Additive Weighting (SAW) Method to recruit production Managers in garments companies by analyzing CV; Impli," International Journal of Science and Business, IJSAB International, vol. 15(1), pages 1-18.

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