M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China

By Dimitri P. Solomatine, Yunpeng Xue. 2004

315 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/5802f1f7e59cfa385831111602040a82c7545edf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment (2014, methodology): “M5 model tree generation requires two different stages (Quinlan 1992; Solomatine and Xue 2004).”
Eager and Lazy Learning Methods in the Context of Hydrologic Forecasting (2006, methodology): “In the present paper instance-based learning is considered in a wider context of machine learning, several methods are explored, their applicability in hydrologic forecasting is tested and their performance is compared to other methods.”
Stochasticity, nonlinearity and forecasting of streamflow processes (2006, methodology): “It has recently been used for rainfall-runoff forecasting problems (e.g., Solomatine and Dulal, 2003; Solomatine and Xue, 2004).”
Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration (2018, methodology): “Contrary to conventional decision tree algorithms, M5Tree combines linear regression functions with the general form of a decision tree at leaves (Rahimikhoob 2016; Solomatine and Xue 2004).”
Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques (2009, methodology): “On the contrary, there is not any overlap of data in the crisp split and the final forecasting output is based on one of local models [Corzo and Solomatine, 2007; Jain and Srinivasulu, 2006; See and Openshaw, 2000; Sivapragasam and Liong, 2005; Solomatine and Xue, 2004].”
Data-driven modelling: some past experiences and new approaches (2008, methodology): “The study by Solomatine & Xue (2004) will be used as an illustration of such an approach.”
Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques (2010, methodology): “87 Solomatine and Xue (2004) used M5 model trees and neural networks in a flood-forecasting 88 problem.”
Prediction of rainfall time series using modular soft computingmethods (2013, methodology): “Solomatine and Xue (2004) used M5 model trees 2 and neural networks in a flood-forecasting problem.”