Identification of dry and rainy periods using telecommunication ...

12 nd International Conference on Urban Drainage, Porto Alegre/Brazil, 10-15 September 2011
Dry Wet
Figure 7 Class errors for two categories Dry and Wet. For the Random Forest, the two Wet classes were
merged to one.
DISCUSSION
In general, our results indicate that all algorithms are able to successfully identify dry and wet periods
based on MWL signals. In Figure 7 we compare the online algorithms S1a, S1b and S2 to RF4, which is
based on similar attributes. For the factor graphs, only an offline algorithm was available. We found that
the quantization had a great influence and therefore provide separate bars for the MWL with fine (bold)
and coarse (shaded) quantization (Figure 7). In general, it can be seen that S2 shows smaller type II errors
than S1a or S1b and out of the type II errors, less correspond to heavy rains (
Figure 5, right). However, we also notice that the cost of fewer type II errors lies in higher
misclassification rates for dry periods. Based on our results, we find that the choice of one
specific algorithm strongly depends on the user’s preferences and the available resources.
First, the absolute performance can be adjusted according to the user’s preference by varying
the respective tuning parameters. For the original (S1) and extended (S2) moving window
algorithms, the fraction of wet periods is a physically-based parameter and should therefore
not necessarily be changed. For the random forests, the composition of the training set and
the weights assigned to each class can be can be tuned. For the Factor graph, the threshold
parameter could be further adjusted to lower the misclassification rate for the wet classes,
which would automatically lead to an increased D misclassification. Second, such parameters
could be optimized by introducing a cost or preference function. Ideally, this should be defined
by the user of the algorithms and will be different for online and offline algorithms as
the user is probably interested in different kind of rainfall data (e.g., real-time information to
control waster water infrastructure vs. generated time series for the dimensioning of waste
water infrastructure). This should also consider the absolute deviation from observed rainfall
volumes.
CONCLUSIONS
In this study, our goal was to develop an appropriate offline and online algorithm for the preprocessing
of MWL data to support urban drainage applications. We compared novel algorithms
based on moving windows, random forests and factor graphs, which is a rather novel
technique from signal processing to those reported in literature. Our results show that an appropriate
online and offline algorithm strongly depends on the quantization and temporal
resolution of the MWL data, which greatly depends on the deployed instruments. With regard
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