Predict anomalies with a daily resolution
Model
The model consists of a feed forward neural network
with one input layer, one hidden layer, and one output layer. The network
was defined to make predictions whether there was any anomalies the next
day or not. The input to the network are daily sums of the planetary magnetic
index Kp extending over the last 6 days.
A database of about 700 anomalies from the Meteosat-3
satellite was used for this purpose. The 700 anomalies were distributed
over almost 500 days. The database was extended to include the same number
of days with no anomalies. The total set, with about 1000 examples, were
divided into to sets with equal number of examples to be used for training
and testing respectively. After optimization the final network has 6 input
neurons, 3 hidden neurons, and 1 output neuron.
Performance
The network correctly predicts 65% of the examples
in the test set. It should be noted that the test set is balanced so that
there are equal numbers of anomaly and no-anomaly days.
The applet
The model is implemented in an applet that can be
run in a Java enabled web browser. The model is loaded to the client so
that it runs locally. The data for a selected period is requested from
the SAAPS server and sent to the client after which the model processes
the data and produces the plot. The applet have been tested on the following
platform and browser combinations:
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Sun Solaris 2.7 with Netscape Communicator 4.7.
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MacOS 8.6 with Microsoft Internet Explorer 4.0.1.
Here it is important to chooses the Apple MRJ in the preferences for Java.
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Microsoft Windows 95 and Netscape 4.08.
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Linux on a Pentium with Netscape Communicator 4.6.
It does not run with Microsoft Internet Explorer on
Windows 95 or Windows 98 and MacOS with Netscape. The reason for this will
be examined.
Run model.
Peter Wintoft, 2000-02-17.