Wave forecasting and monitoring during very severe cyclone Phailin in the Bay of Bengal

Wave fields, both measured and forecast during the very severe cyclone Phailin, are discussed in this communication. Waves having maximum height of 13.54 m were recorded at Gopalpur, the landfall point of the cyclone. The forecast and observed significant wave heights matched well at Gopalpur with correlation coefficient of 0.98, RMS error of 0.35 m and scatter index of 14%. Forecasts were also validated in the open ocean and found to be reliable (scatter index < 15%). The study also revealed the presence of Southern Ocean swells with a peak period of 20-22 sec hitting Gopalpur coast along with the cyclone-generated waves

Performance of the ocean state forecast system at Indian National Centre for Ocean Information Services

The reliability of the operational Ocean State Forecast system at the Indian National Centre for Ocean Information Services (INCOIS) during tropical cyclones that affect the coastline of India is described in this article. The performance of this system during cyclone Thane that severely affected the southeast coast of India during the last week of December 2011 is reported here. Spec-tral wave model is used for forecasting the wave fields generated by the tropical cyclone and vali-dation of the same is done using real-time automated observation systems. The validation results indicate that the forecasted wave parameters agree well with the measurements. The feedback from the user community indicates that the forecast was reliable and highly useful. Alerts based on this operational ocean state forecast system are thus useful for protecting the property and lives of the coastal communities along the coastline of India. INCOIS is extending this service for the benefit of the other countries along the Indian Ocean rim

Wave forecasting and monitoring during very severe cyclone Phailin in the Bay of Bengal

Wave fields, both measured and forecast during the very severe cyclone Phailin, are discussed in this communication. Waves having maximum height of 13.54 m were recorded at Gopalpur, the landfall point of the cyclone. The forecast and observed significant wave heights matched well at Gopalpur with correlation coefficient of 0.98, RMS error of 0.35 m and scatter index of 14%. Forecasts were also validated in the open ocean and found to be reliable (scatter index &#60; 15%). The study also revealed the presence of Southern Ocean swells with a peak period of 20-22 sec hitting Gopalpur coast along with the cyclone-generated waves

Implementation of projected clustering based on SQL queries and UDFs in relational databases

Projected clustering is one of the clustering approaches that determine the clusters in the subspaces of high dimensional data. Although it is possible to efficiently cluster a very large data set outside a relational database, the time and effort to export and import it can be significant. In commercial RDBMSs, there is no SQL query available for any type of subspace clustering, which is more suitable for large databases with high dimensions and large number of records. Integrating clustering with a relational DBMS using SQL is an important and challenging problem in todays world of Big Data. Projected clustering has the ability to find the closely correlated dimensions and find clusters in the corresponding subspaces. We have designed an SQL version of projected clustering which helps to get the clusters of the records in the database using a single SQL statement which in itself calls other SQL functions defined by us. We have used PostgreSQL DBMS to validate our implementation and have done experimentation with synthetic as well as real data

K-Medoid Clustering for Heterogeneous DataSets

AbstractRecent years have explored various clustering strategies to partition datasets comprising of heterogeneous domains or types such as categorical, numerical and binary. Clustering algorithms seek to identify homogeneous groups of objects based on the values of their attributes. These algorithms either assume the attributes to be of homogeneous types or are converted into homogeneous types. However, datasets with heterogeneous data types are common in real life applications, which if converted, can lead to loss of information. This paper proposes a new similarity measure in the form of triplet to find the distance between two data objects with heterogeneous attribute types. A new k-medoid type of clustering algorithm is proposed by leveraging the similarity measure in the form of a vector. The proposed k-medoid type of clustering algorithm is compared with traditional clustering algorithms, based on cluster validation using Purity Index and Davies Bouldin index. Results show that the new clustering algorithm with new similarity measure outperforms the k-means clustering for mixed datasets