Development of a low-flow forecasting model using the M5 machine learning method

Abstract By using the M5 machine learning method and analysis of the recorded recession streamflow data, we modelled the streamflow recession coefficient k as a function of the flow rate at which the 7-day low flow forecast is made and the decrease in the flow rate from the previous day. Low flow forecasting models for seven of the Slovenian tributaries of the Sava River were developed and verification of the results showed improved accuracy of the models compared to when using a single-valued recession coefficient. From the structure of the models, we can also learn that the degree of the flow rate change in the last 24 hours can tell us more about the streamflow recession dynamics in the next seven days than the flow rate at the time when the forecast is made.

[1]  W. Langbein.  Some channel‐storage studies and their application to the determination of infiltration , 1938 .

[2]  F. R. Hall.  Base‐Flow Recessions—A Review , 1968 .

[3]  T. McMahon,et al.  Evaluation of automated techniques for base flow and recession analyses , 1990 .

[4]  J. R. Quinlan.  Learning With Continuous Classes , 1992 .

[5]  L. Tallaksen.  A review of baseflow recession analysis , 1995 .

[6]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[7]  Keith Beven,et al.  Using interactive recession curve analysis to specify a general catchment storage model , 1997 .

[8]  R. Moore.  Storage-outflow modelling of streamflow recessions, with application to a shallow-soil forested catchment , 1997 .

[9]  Christian W. Dawson,et al.  Hydrological modelling using artificial neural networks , 2001 .

[10]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[11]  D. Solomatine,et al.  Model trees as an alternative to neural networks in rainfall—runoff modelling , 2003 .

[12]  Dimitri P. Solomatine,et al.  M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China , 2004 .

[13]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[14]  Dimitri P. Solomatine,et al.  Neural networks and M5 model trees in modelling water level-discharge relationship , 2005, Neurocomputing.