Knowledge Discovery from Earth Science Data

Mining the knowledge from climate data is one of the important issues due the rapid development and the extensive use of data acquisition technology. Earth science data increases tremendously, it is containing a large amount of information and knowledge. In this paper we used earth science data which is downloaded from the NASA website. The Earth science data has characteristics of mass, high dimensions, spatial co-relations. The data consists of time series measurements of various climate variables such as temperature, pressure, precipitation, humidity, direction and speed of wind and so forth. Any sudden change in parameters cause change in weather and also changes in parameters patterns behaviour. These sudden changes in parameters pattern do not conform to the general behaviour of the data set, these nonconforming patterns are often referred to as anomalies. Anomalies in the climatic variable cause ecosystem disturbance events such as forest fires, droughts, floods, and deforestation. Anomaly detection is a problem of finding unexpected patterns in a data set. In June 2013, a multi-day cloud burst centred on the North Indian state of Uttarakhand caused devastating floods and landslides in the country's worst natural disaster. Some neighbours area of Uttarakhand was experienced heavy rainfall. In this paper, we show the co-relations between the climatic parameters and analysed all parameters that affects the climates with the help of our proposed algorithm, which has the capability of discovering anomalies in the data set, in addition, analyse and identify significant changes or anomalies in the Uttarakhand climate data set. We also proposed anomalies detection algorithm based on Min-Max Method.