Main Article Content
In each research field, quality control is the crucial part in order to ensure that the data collected is trustworthy. Quality control management helps in detecting abnormal behavior, monitor the health of detecting instruments. Argo Float is an ocean instrument which gives a lot of information regarding the conditions of the ocean. The information obtained has its value only if the quality of the parameter measuring procedure involves good quality tools. So, maintaining the quality of the procedure is as important as analyzing the obtained data. The salinity and the temperature data from Argo floats which collect data from in situ regions of ocean need to be scrutinized for anomalous values and examined to find out the reason behind the abnormal values recorded using a new method proposed by using Alpha convex hull. The data collected is used to construct an alpha shape polygon which is fine tuned to obtain best possible results. This method segregates by representing good data inside the polygon and the bad data outside the polygon. The proposed intelligent system is observed to detect the anomalous data occurring due to various problems such as sensor corrosion, bias ,spikes etc. This method aims to perform quality control for oceanographic data of argos at present but aims to work on different sources. The main advantages of the proposed system can be seen as the ability to identify anomalous data from in situ bulk data in minimal steps and to be applicable for cumulative data of various depths together.