abstract = "The main problem in rainfall/runoff modeling is to
obtain data about the catchment with sufficient
accuracy. Since self-learning tools only need knowledge
about rainfall and runoff, they can offer a good
alternative to classical model. The present study
focuses on Lindenborg, a Danish catchment situated in
the northern part of Jutland, between Hobro and Alborg.
It is characterized by high groundwater contribution
and thus a very persistent flow regime. The tools used
were artificial neural networks (ANN) and genetic
programming (GP). The purpose was to compare the
efficiency of these tools with a classic lumped model
(NAM) and a naive prediction (i.e. the runoff does not
change between one day and the next one). The study
with GP was oriented in two directions : the prediction
of the runoff, and the prediction of the variation in
the runoff. In both cases GP was given the rainfall and
runoff of the past days, and it was assumed that the
rainfall was predicted without any error for the target
day. Each strategy has its own advantages. Predicting
the variation is considered to be closer to the
relationships given by physics, whereas predicting the
runoff takes in account the large auto-correlation of
the runoff time series. Since it is difficult to
predict the upper boundary of runoff, the ANN worked
exclusively with the time variation. The variation in
runoff is less likely to saturate the network than the
runoff itself, especially in this catchment where the
dynamics are relatively slow. Therefore, the
sensitivity of the prediction is increased. Time lag
recurrent network (TLRN) were used for this study as
they allow to take in account smoothed version of the
past time series, both in the input and the hidden
layers. The comparison of the different models was
based on the Pearson coefficient of correlation, which
gives a good overview of the performance of the
prediction. This study was realized in relationship
with the Department of Hydrodynamics and Water
Resources of DTU as a special course for the Master of
Science in Environmental Engineering.",
