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. Copyright © 2007 IAHS Press.