Fig. 1

Historical distribution of Argo float placement positions"

Fig. 2

Technology roadmap"

Fig. 3

The schematic diagrams of the quality control checks. (a) Range check; (b) increasing pressure check; (c) temperature inversion check; (d) density inversion check; (e) profile spike check; (f) climatology check. The red line in the figure indicates the change in the anomalous data, and the blue line indicates the constructed thresholds"

Fig. 4

The flow chart of RS-MLP"

Fig. 5

Quality control effectiveness of the machine learning models. (a) Original distribution of test set data; (b) predicted results of LOF; (c) predicted results of IF; (d) predicted results of SVM; (e) predicted results of DT; (f) predicted results of KNN; (g) predicted results of RF; (h) predicted results of SGD; (i) predicted results of GNB; (j) predicted results of XGB; (k) predicted results of LGB; (l) predicted results of CatBoost; (m) predicted results of NGB; (n) predicted results of MLP"

Tab. 1

Results of evaluation metrics for the machine learning models"

Fig. 6

The ROC curves as (a) and the AUC values as (b) of the machine learning models"

Tab.2

Results of evaluation metrics for the machine learning models under the cross-validation approach"

Fig. 7

Quality control effectiveness of RS-MLP. (a) Original distribution of test set data; (b) predicted results of rule set; (c) predicted results of MLP; (d) predicted results of MFP; (e) predicted results of GCH; (f) predicted results of RS-MLP"

Tab. 3

Results of evaluation metrics for RS-MLP"

Fig. 8

The ROC curves as (a) and the AUC values as (b) of the RS-MLP"

Tab. 4

Results of evaluation metrics for RS-MLP under the cross-validation approach"

Fig. 9

Scattered distribution density of anomalous data"

Fig. 10

Results of the evaluation metrics for the machine learning models at different depths. (a) Results of the TPR at different depths; (b) results of the TNR at different depths; (c) results of the AUC at different depths"