(Last updated 15 November 2005)
Prof Rob Wilby, Drs Bob Abrahart, Asaad Shamseldin, Linda See and Christian Dawson would like to welcome you to the ANNEXG home page. The intention of ANNEXG is to initiate a project to evalaute the effectiveness of artificial neural neworks in rainfall-runoff modelling/flow forecasting.
Our intention is to provide a standard rainfall-runoff data set (available below) that will be used by all participants to create a number of neural network models. We then hope to integrate the model results to examine the skill of invidividual members, as well as the ensemble forecast(s) for the catchment.
All the data you send back to us will be anonymous. In other words, when the model statistics are calculated and published, there will be no indication as to who has produced the most accurate model (although parameters will be published so you should be able to work out which model(s) is your own!).
The latest study - 2005 / 2006
Following on from the 2001 / 2002 experiments (see below) a new data set is provided which is coded at a 6 hourly time interval. The data sets and rules can be downloaded directly from this page below.
Model results will be evaluated using statistics calculated by the HydroTest web site at: www.hydrotest.org.uk.
The 'rules' are available as a Word document from here .
All the data are presented in Excel format and can be download as a single zipped file from here . Alternatively, you can download the individual files below.
The calibration data is split into two files; Train1.xls and Train2.xls .
The test data is available as Test.xls .
Please return all results to Christian Dawson by 28 April 2006.
The first ANNEXG experiments were undertaken during 2001 / 2002. This exercise involved the dissemination of a benchmark catchment data set to seventeen neurohydrologists world wide. Each was given the freedom to develop up to two ANN models for t+1 and t+3 days ahead forecasting in an unknown catchment. An additional motivation for this exercise was to investigate the potential of ensemble forecasting to improve forecast accuracy and, taking this work further, using ensembles to provide confidence in modelling performance.
The results were presented at the 8th BHS National Hydrology Symposium at the University of Birmingham, UK (8 - 11 September 2002). You can download a copy of the paper from here.
You can access the original benchmark data set as a zip file here.
You can view the 'rules' that the participants had to follow from here.
If you have any comments / questions / queries please feel free to contact Christian Dawson at : C.W.Dawson1@lboro.ac.uk