NDLI: Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression

Content Provider	Springer Nature Link
Author	Goyal, Manish Kumar
Copyright Year	2014
Abstract	The forecast of the sediment yield generated within a watershed is an important input in the water resources planning and management. The methods for the estimation of sediment yield based on the properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment yield. The focus of present study is to deal with the development of decision tree based M5 Model Tree and wavelet regression models for modeling sediment yield in Nagwa watershed in India. A comparison is also performed with the artificial neural network (ANN) model for streamflow forecasting. The root mean square errors (RMSE), Nash-Sutcliff efficiency index (N-S Index), and correlation coefficient (R) statistics are used for the statistical criteria. A comparative evaluation of the performance of M5 Model Tree and wavelet regression versus ANN clearly shows that M5 Model Tree and wavelet regression can prove more useful than ANN models in estimation of sediment yield. Further, M5 model tree offers explicit expressions for use by design engineers.
Starting Page	1991
Ending Page	2003
Page Count	13
File Format	PDF
ISSN	09204741
Journal	Water Resources Management
Volume Number	28
Issue Number	7
e-ISSN	15731650
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-04-02
Publisher Institution	European Water Resources Association (EWRA)
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Streamflow M5 model tree Discrete wavelet transform Regression Forecast Hydrogeology Hydrology/Water Resources Geotechnical Engineering & Applied Earth Sciences Atmospheric Sciences Civil Engineering Environment
Content Type	Text
Resource Type	Article
Subject	Water Science and Technology Civil and Structural Engineering

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