River suspended sediment modelling using the CART model: A comparative study of machine learning techniques

Suspended sediment load (SSL) modelling is an important issue in integrated environmental and water resources management, as sediment affects water quality and aquatic habitats. Although classification and regression tree (CART) algorithms have been applied successfully to ecological and geomorpholo...

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Bibliographic Details
Published in:The Science of the total environment 2018-02, Vol.615, p.272-281
Main Authors: Choubin, Bahram, Darabi, Hamid, Rahmati, Omid, Sajedi-Hosseini, Farzaneh, Kløve, Bjørn
Format: Article
Language:English
Subjects:
Adaptive neuro-fuzzy inference system
Classification and regression trees
Haraz watershed
Multi-layer perceptron neural network
Support vector machine
Suspended sediment load
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Summary:Suspended sediment load (SSL) modelling is an important issue in integrated environmental and water resources management, as sediment affects water quality and aquatic habitats. Although classification and regression tree (CART) algorithms have been applied successfully to ecological and geomorphological modelling, their applicability to SSL estimation in rivers has not yet been investigated. In this study, we evaluated use of a CART model to estimate SSL based on hydro-meteorological data. We also compared the accuracy of the CART model with that of the four most commonly used models for time series modelling of SSL, i.e. adaptive neuro-fuzzy inference system (ANFIS), multi-layer perceptron (MLP) neural network and two kernels of support vector machines (RBF-SVM and P-SVM). The models were calibrated using river discharge, stage, rainfall and monthly SSL data for the Kareh-Sang River gauging station in the Haraz watershed in northern Iran, where sediment transport is a considerable issue. In addition, different combinations of input data with various time lags were explored to estimate SSL. The best input combination was identified through trial and error, percent bias (PBIAS), Taylor diagrams and violin plots for each model. For evaluating the capability of the models, different statistics such as Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and percent bias (PBIAS) were used. The results showed that the CART model performed best in predicting SSL (NSE=0.77, KGE=0.8, PBIAS
ISSN:0048-9697
1879-1026
DOI:10.1016/j.scitotenv.2017.09.293