Page History

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<periodCoefficientSet>
  <!-- periodCoefficientSet A -->
  <period>
    <startDateTime date="2002-01-01" time="00:00:00"/>
    <endDateTime date="2003-01-01" time="00:00:00"/>
  </period>
  ...
</periodCoefficientSet>
<periodCoefficientSet>
  <!-- periodCoefficientSet B -->
  <period>
    <startDateTime date="2003-01-01" time="00:00:00"/>
    <endDateTime date="2004-01-01" time="00:00:00"/>
  </period>
  ...
</periodCoefficientSet>
<periodCoefficientSet>
  <!-- periodCoefficientSet C -->
  <period>
    <startDateTime date="2010-01-01" time="00:00:00"/>
    <endDateTime date="2011-01-01" time="00:00:00"/>
  </period>
  ...
</periodCoefficientSet>

Validation rules

The concept of the validation rules was introduced as a solution for a common problem in operational situations when using aggregation transformations. When for example an aggregation was done over an entire year a single missing value in the input values would cause that the yearly average was also a missing value.

The validation rules provide a solution for these types of situations. It allows to configure in which cases an output value should be computed although the input contains missing values and/or doubtful values.

The validation rules are optional in the configuration and can be used to define the outputflag and the custom flagsource of the output value based on the number of missing values/unreliables values and/or the number of doubtful values in the used input values. The available output flags are reliable, doubtful and missing.

With these rules it is possible to define for example that the output of the transformation is reliable if less than 10% of the input is unreliable and/or missing and that if this percentage is above 10% that in that case the output should be a missing value.

It is important to note that input values which are missing and input values which are marked as unreliable are treated the same. Both are seen as missing values by the validation rules.

Below the configuration of the basic example which was described above.

				<validationRule>
					<inputMissingPercentage>10</inputMissingPercentage>
					<outputValueFlag>reliable</outputValueFlag>
				</validationRule>
				<validationRule>
					<inputMissingPercentage>100</inputMissingPercentage>
					<outputValueFlag>missing</outputValueFlag>
				</validationRule>

The configured validation rules are applied in the following way. The first validation rules are applied first. In the example above the first rule is that if 10% or less of the input is missing (or unreliable) that the output flag will be set to reliable. If the input doesn't meet the criteria for the first rule the transformation module will try to apply the second rule. In this case the second rule will always apply because a percentage of 100% is configured.

Configuring a rule with a percentage of 100% is a recommended way of configuring the validation rules. By default if validation rules are configured and none of the configured rules are valid the output will be set to missing. Which means that in this case the second rule of 100% was not necessary because it is also the default hard-coded behaviour of the system.

But for the users of the system it is more understandable if the behaviour of the aggregation is configured instead of a hard-coded fallback mechanism in the software.

To explain the validation rules a bit more a more difficult example will explained. Let's say that we would like configure our aggregation in such a way that the following rules are applied:

1 if the percentage of missing and/or unreliable values is less than 15% the output should be reliable.
2 if the percentage of missing values is less than 40% the output should be doubtful.
3 in all other cases the output should be a missing value.

Below shows a configuration example in which the rules above are implemented.

<validationRule>
  <inputMissingPercentage>15</inputMissingPercentage>
  <outputValueFlag>reliable</outputValueFlag>
</validationRule>
<validationRule>
  <inputMissingPercentage>40</inputMissingPercentage>
  <outputValueFlag>doubtful</outputValueFlag>
</validationRule>
<validationRule>
  <inputMissingPercentage>100</inputMissingPercentage>
  <outputValueFlag>missing</outputValueFlag>
</validationRule>

The example shows that in total 3 validation rules were needed. The first rule checks if less than 15% of the input is missing/unreliable. If this is not the case than it will be checked if the second rule can be applied. The second rule states that if less than 40% of the input is missing that in that case the output flag should be set to doubtful. The last rule takes care of all the other situations. Note that it has a percentage configured of 100%. Which means that this rule will be applied. However because 2 rules are defined above this rule FEWS will always try to apply these rules first before applying this rule.

In some cases one would like to differ between situations in which the outputflag is the same. In the example above if all of the input values were reliable the output is marked as reliable. But if for example 10% of the input values were unreliable the output is also marked as reliable.

It would be nice if the user of the system would be able to see in the GUI of FEWS why the input was marked reliable. Were there missing values in the input or not? Is the output based on a few missing values?

To make this possible the concept of the custom flag source was added to the validation rules. In addition to configuring an output flag it is also possible to configure a custom flag source. In the table of the Timeseriesdialog the custom flag source can be made visible by pressing ctrl + shift + j. This will make a new column in the table visible in which the custom flag source ids are shown. In the graph itself it also possible to make the custom flag sources visible by pressing ctrl + alt + v. To use the custom flagsources a file CustomFlagSources.xml should be added to the RegionConfig directory. For details see 27 CustomFlagSources In this file the custom flag sources should be defined. By configuring several rules which has the same outputflag but a different custom flagsource it is possible to make a difference between situations in which the outputflag is the same.

Below an example in which the output is reliable when there are no missing values in the input and when the percentage if missing values is less than 15%. However in the first case the output doesn't get a custom flagsource assigned while in the second case the output gets a custom flagsource assigned which is visible in the GUI to indicate that a output value was calculated but that missing values were found in the input.

<validationRule>
  <inputMissingPercentage>0</inputMissingPercentage>
  <outputValueFlag>reliable</outputValueFlag>
</validationRule>
<validationRule>
  <inputMissingPercentage>15</inputMissingPercentage>
  <outputValueFlag>reliable</outputValueFlag>
  <outputCustomFlagSourceId>CA</outputCustomFlagSourceId>
</validationRule>
<validationRule>
  <inputMissingPercentage>40</inputMissingPercentage>
  <outputValueFlag>doubtful</outputValueFlag>
</validationRule>
<validationRule>
  <inputMissingPercentage>100</inputMissingPercentage>
  <outputValueFlag>missing</outputValueFlag>
</validationRule>

Finally it is also possible to define validation rules based on the number of doubtful values in the input. It is important to note that missing values and unrealiable values found in the input are also counted as doubtful values. It is even possible to define validation rules based on a combination of an allowed percentage of unreliable/missing values and doubtful values. The sequence of applying the rules is also in this case the order in which the rules are configured. The first rule which applies to the
current situation is used.

Let's say for example that we also want rules to be defined for the doubtful input values. For example when only a small number of input values are doubtful we still want the output to be reliable. Otherwise we would like to have the output to be doubtful but with an custom flag source which give us an indication of how many of the input values were doubtful.

Below a configuration example

<validationRule>
  <inputDoubtfulPercentage>10</inputDoubtfulPercentage>
  <inputMissingPercentage>0</inputMissingPercentage>
  <outputValueFlag>reliable</outputValueFlag>
</validationRule>
<validationRule>
  <inputDoubtfulPercentage>30</inputDoubtfulPercentage>
  <inputMissingPercentage>0</inputMissingPercentage>
  <outputValueFlag>doubtful</outputValueFlag>
  <outputCustomFlagSourceId>D1</outputCustomFlagSourceId>
</validationRule>
<validationRule>
  <inputDoubtfulPercentage>60</inputDoubtfulPercentage>
  <inputMissingPercentage>0</inputMissingPercentage>
  <outputValueFlag>doubtful</outputValueFlag>
  <outputCustomFlagSourceId>D2</outputCustomFlagSourceId>
</validationRule>
<validationRule>
  <inputDoubtfulPercentage>100</inputDoubtfulPercentage>
  <inputMissingPercentage>0</inputMissingPercentage>
  <outputValueFlag>doubtful</outputValueFlag>
  <outputCustomFlagSourceId>D3</outputCustomFlagSourceId>
</validationRule>
<validationRule>
  <inputMissingPercentage>15</inputMissingPercentage>
  <outputValueFlag>reliable</outputValueFlag>
  <outputCustomFlagSourceId>CA</outputCustomFlagSourceId>
</validationRule>
<validationRule>
  <inputMissingPercentage>40</inputMissingPercentage>
  <outputValueFlag>doubtful</outputValueFlag>
</validationRule>
<validationRule>
  <inputMissingPercentage>100</inputMissingPercentage>
  <outputValueFlag>missing</outputValueFlag>
</validationRule>

The explanation above gave an good idea of the possibilities of the use of the validation rules.

In the examples above the inputMissingValuePercentage and the inputDoubtfulPercentage was configured hard-coded in the configuration file. However it is also possible to make a reference to an attribute of a location. To reference to an attribute the referenced attribute should be placed within @.

<inputMissingPercentage>@MV@</inputMissingPercentage>

For example to reference the attribute MV for the inputMissingValuePercentage the configuration should be like.

To explain the concept of the validation rules more the table below shows the input time series and the output time series of an aggregation accumulative tranformation which uses the validation rules which are shown above in the last eexample.

The first output value is set to doubtful. Because in this case the total percentage of missing values is 25%. Which means that the following rule is applied.

<validationRule>
  <inputMissingPercentage>40</inputMissingPercentage>
  <outputValueFlag>doubtful</outputValueFlag>
</validationRule>

The second output value is a missing value because in this case the percentage of missing values is equal to 50%. This means that in this case the following rule will be appplied.

<validationRule>
  <inputMissingPercentage>100</inputMissingPercentage>
  <outputValueFlag>missing</outputValueFlag>
</validationRule>

The third output value is set to doubtful. The input doesn't contain missing values but has a single doubtful input value. The percentage of doubtful values in the input is therefore 25% which means that the following rule will be applied.

<validationRule>
  <inputDoubtfulPercentage>30</inputDoubtfulPercentage>
  <inputMissingPercentage>0</inputMissingPercentage>
  <outputValueFlag>doubtful</outputValueFlag>
  <outputCustomFlagSourceId>D1</outputCustomFlagSourceId>
</validationRule>

the fourth and last output value has set to reliable with no output flag. In this case all in the input values are reliable. In this case the first rule
is applied (in the case when all of the input values are reliable the first rule always applies).

Manual Edits

Since FEWS 2017.02 it is possible to configure if manual edits should be preserved. This setting applies to all transformations that are configured. The default is false. For an example configuration see:

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