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Status of Machine Learning for Argo QC #6
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At Ifremer/LOPS, we've tried the following: Target variables:Alarm status (True, False) of the ISAS13 test against climatology for one PSAL measurement Features:A "patch" of variables from the same profile as the target as well as from profiles before and after (+/- 2). Variables used: TEMP, PSAL, SIG0 and PRES. ML methodRandom forest Dataset usedArgo snapshot from 2016/02 and ISAS team QC logs. Overall performance or difficulties encountered
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Not sure if that helps or if I totally understand but would it make sense to consider using several climatology products and e.g. counting the # of alarms (e.g. 0/6 vs 6/6) and setting a threshold? I used to do something like that in the MITprof QC for ECCO (I was using the min of cost functions if I recall). |
@gaelforget this is a good suggestion that we started to experiment as well: taking a final decision on the basis of several QC test outcomes. |
I'd like to open a discussion thread to get the status of developments with regard to the use of Machine Learning techniques in Argo QC procedures.
Different groups may have started to explore this possibility and it would be constructive to get here the status of these efforts, to avoid duplicates and to get feedback.
This could include a description of:
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