Neural networks and M5 model trees in modelling water level-discharge relationship

By B. Bhattacharya, Dimitri P. Solomatine. Neurocomputing, 2005

312 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/5f8efcdd8765109e28c69c107f074cbc6dc5673c

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Modelling stage—discharge relationships affected by hysteresis using the Jones formula and nonlinear regression (2006, methodology): “Recently, Bhattacharya & Solomatine (2005) presented a stage–discharge model based on ANN and an M5 model tree.”
Data-driven modelling: some past experiences and new approaches (2008, unknown): “Jain & Srinivasulu (2006) applied a mixture of neural networks and conceptual techniques to model the different segments of a decomposed flow hydrograph.”
Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, methodology): “Considering an N-sample training matrices characterized by (input) patterns/attributes associated with a predictand (Deo et al. 2017a), the M5Tree model aims to build a model concerning a target value of training case and the input attributes (Bhattacharya and Solomatine 2005).”
Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree (2010, methodology): “M5 model tree (Quinlan 1992) based modeling is not as popular as ANN but has been proved to be efficient and robust in water resource applications (Solomatine and Xue 2004; Bhattacharya and Solomatine 2005; Shrestha and Solomatine 2006; as well as Sakhare and Deo 2009).”
A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers (2020, background): “The red s can be formulated as follows [67]:”