DOI QR코드

DOI QR Code

Estimation of Water Quality of Fish Farms using Multivariate Statistical Analysis

  • Ceong, Hee-Taek (Department of Digital Convergence, Chonnam National University) ;
  • Kim, Hae-Ran (Department of Digital Convergence, Chonnam National University)
  • Received : 2011.06.07
  • Accepted : 2011.07.08
  • Published : 2011.08.31

Abstract

In this research, we have attempted to estimate the water quality of fish farms in terms of parameters such as water temperature, dissolved oxygen, pH, and salinity by employing observational data obtained from a coastal ocean observatory of a national institution located close to the fish farm. We requested and received marine data comprising nine factors including water temperature from Korea Hydrographic and Oceanographic Administration. For verifying our results, we also established an experimental fish farm in which we directly placed the sensor module of an optical mode, YSI-6920V2, used for self-cleaning inside fish tanks and used the data measured and recorded by a environment monitoring system that was communicating serially with the sensor module. We investigated the differences in water temperature and salinity among three areas - Goheung Balpo, Yeosu Odongdo, and the experimental fish farm, Keumho. Water temperature did not exhibit significant differences but there was a difference in salinity (significance <5%). Further, multiple regression analysis was performed to estimate the water quality of the fish farm at Keumho based on the data of Goheung Balpo. The water temperature and dissolved-oxygen estimations had multiple regression linear relationships with coefficients of determination of 98% and 89%, respectively. However, in the case of the pH and salinity estimated using the oceanic environment with nine factors, the adjusted coefficient of determination was very low at less than 10%, and it was therefore difficult to predict the values. We plotted the predicted and measured values by employing the estimated regression equation and found them to fit very well; the values were close to the regression line. We have demonstrated that if statistical model equations that fit well are used, the expense of fish-farm sensor and system installations, maintenances, and repairs, which is a major issue with existing environmental information monitoring systems of marine farming areas, can be reduced, thereby making it easier for fish farmers to monitor aquaculture and mariculture environments.

Keywords

References

  1. National Fisheries Research and Development Institute, 2009, A Study on the standardization of Aquaculture Industry
  2. Kim, Ceong, Han, Kang and Jang, 2007. Aqua Data Sensing System for Eco-friendly Aquaculture Products. 2nd International Conference on Ubiquitous Information
  3. Ceong, Park and Han, 2007. IT Convergence Application System for Eco Aquafarm. 07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
  4. Korea Hydrographic and Oceanographic, 2009. Measurement data for Balpo and Odongdo Coastal http://http://www.khoa.go.kr/
  5. Kim and Ceong, 2010. Statistical Analysis of Water Quality in a Land-based Fish Farm. The Korea Institute of Electronic Communication Sciences 5(6)
  6. Liu, C.W., Lin, K.H.,Kuo,Y.M., 2003. Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Sci. Tot. Environ. 313, 77-89 https://doi.org/10.1016/S0048-9697(02)00683-6
  7. Singh, Malik and Mohan, 2004. Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River(India) - a case study, Water Research 38 : 3980-3992 https://doi.org/10.1016/j.watres.2004.06.011
  8. Hair et al., 1998. Multivariate data analysis. Prentice Hall, New Jersey, USA.
  9. Kim, Koh, Ko and Yeo, 2008. Prediction of Nitrate Contamination of Groundwater in the Northern Nonsan area Using Multiple Regression Analysis. Korea. Geoscience and Mineral Res. 13(5)
  10. S. Shrestha, F. Kazama, 2007. Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environment Modelling & Software 22, 464-475 https://doi.org/10.1016/j.envsoft.2006.02.001