Detecting and Correcting the Degradations of Sensors on Argo Floats Using Artificial Neural Networks
Argo floats are autonomous floats designed to measure temperature and salinity of the world oceans. Once deployed these floats goes to as deep as 2000 m and while coming up measure temperature and salinity of the underlying ocean automatically. These floats act as a substitute to the ship-based data sets and currently as many as ~3800 are active in the global oceans. These instruments being autonomous in nature, measure and transmit data seamlessly irrespective of the weather, season, and region. However, the salinity sensors on these floats are sensitive to bio-fouling and can cause degradation to the data. As these are one time deployed and data is continuously obtained they are not available for calibration unlike the instruments on the ship. In this work ANN is used to check the degradation of the sensors and correct the same so that the data can be use in scientific analysis.
KeywordsArtificial neural network Back propagation Feed forward Argo floats Salinity CTD
The authors wish to thank Dr. SSC Shenoi, Director, INCOIS, Dr. TVS Udaya Bhaskar (Scientist-“E”), and J. Pavan Kumar (Scientist-“B”), INCOIS, Hyderabad for their support and guidance throughout the work in this project and for preparing the manuscript. Authors also thank Mrs. K.S. Deepthi (Assistant Professor), ANITS, Professor S.C. Satapathy (Head of Department), ANITS, Visakhapatnam for their support in the College and work.
- 1.Argo Science Team, 1998. On the design and implementation of Argo: An initial plan for a global array of profiling floats. International CLIVAR Project Office Report 21, GODAE Report 5. GODAE International Project Office, Melbourne, Australia, 32 pp.Google Scholar
- 2.DR. Yashpal Singh, Alok Singh Chauhan, “Neural Networks in Data mining”, Journal of Theoretical and Applied Information Technology, 2005–2009, pp. 37–42.Google Scholar
- 3.David Leverington, Associate Professor of Geosciences, “A Basic introduction to feed forward back propagation neural networks”, 2016, pp. 1–25.Google Scholar
- 4.D. Prashanth Kumar, B. Yakhoob, N. Raghu “Improving Efficiency of Data Mining through Neural Networks”, Conference paper October 2013, pp. 14–18.Google Scholar
- 5.Boyer, T.P., J.I. Antonov, O.K. Baranova, C. Coleman, H.E. Garcia, A. Grodsky, D.R. Johnson, R.A. Locarnini, A.V. Mishonov, T.D. O’Brien, C.R. Paver, J.R. Reagan, D. Seidov, I.V. Smolyar, M.M. Zweng, 2013, World Ocean Database 2013. Sydney Levitus, Ed.; Alxey Mishonov, Technical Ed.; NOAA Atlas NESDIS 72, 209 pp.Google Scholar
- 6.Haykin, S., Neural Networks, Prentice Hall International Inc., 1999.Google Scholar
- 7.Vladimir Krasnopolsky, Sudhir Nadiga, Avichal Mehra, Eric Bayler, and David Behringer., “Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations”, Volume 2016, pp. 1–9.Google Scholar
- 8.Jiawei Han, Micheline Kamber, Jian Pei, “Data mining Concepts and techniques” Third edition, Elsevier 2012, pp. 398–408.Google Scholar