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

Complexity 2021:1-19 (2021)
  Copy   BIBTEX


Suspended sediment modeling is an important subject for decision-makers at the catchment level. Accurate and reliable modeling of suspended sediment load is important for planning, managing, and designing of water resource structures and river systems. The objective of this study was to develop artificial intelligence- based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI-based models, that is, support vector machine, adaptive neurofuzzy inference system, feed-forward neural network, and one conventional multilinear regression modes, were used for SSL modeling. Besides, four different ensemble methods, neural network ensemble, ANFIS ensemble, weighted average ensemble, and simple average ensemble, were developed by combining the outputs of the four single models to improve their predictive performance. The study used two-year discharge and SSL data for training and verification of the applied models. Determination coefficient and root mean square error 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.



    Upload a copy of this work     Papers currently archived: 91,998

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

A New Wrapped Ensemble Approach for Financial Forecast.Hua Zhang, BaoLong Yue & Yun Ling - 2014 - Journal of Intelligent Systems 23 (1):21-32.
Classical Probability, Shakespearean Sonnets, and Multiverse Hypotheses.James Goetz - 2006 - International Society for Complexity, Information, and Design Archive 2006.
Ensemble prospectism.Kim Kaivanto - 2017 - Theory and Decision 83 (4):535-546.


Added to PP

9 (#1,255,090)

6 months
5 (#641,095)

Historical graph of downloads
How can I increase my downloads?

Citations of this work

No citations found.

Add more citations

References found in this work

No references found.

Add more references