Support Vector Regression Model of Chlorophyll-*a* during Spring Algal Bloom in Xiangxi Bay of Three Gorges Reservoir, China

Affiliation(s)

College of Chemistry & Life Science, Three Gorges University, Yichang, China.

College of Hydroelectric & Environment, Three Gorges University, Yichang, China.

Engineering Research Center of Eco-Environment in Three Gorges Reservoir Region, Ministry of Education, Three Gorges University, Yichang, China.

College of Chemistry & Life Science, Three Gorges University, Yichang, China.

College of Hydroelectric & Environment, Three Gorges University, Yichang, China.

Engineering Research Center of Eco-Environment in Three Gorges Reservoir Region, Ministry of Education, Three Gorges University, Yichang, China.

ABSTRACT

To study the relationship between chlorophyll-a and environmental variables during spring algal bloom in Xiangxi Bay of Three Gorges Reservoir, the support vector regression (SVR) model was established. In surveys, 11 stations have been investigated and 264 samples were collected weekly from March 4 to May 13 in 2007 and February 16 to May 10 in 2008. The parameters in SVR model were optimized by leave one out cross validation. The squared correlation coefficient*R*^{2} and the cross validated squared correlation coefficient *Q*^{2} of the optimal SVR model are 0.8202 and 0.7301, respectively. Compared with stepwise multiple linear regression and back propagation artificial neural network models using external validation, the SVR model has been shown to perform well for regression with the predictive squared correlation coefficient *R*^{2}_{pred} value of 0.7842 for the test set.

To study the relationship between chlorophyll-a and environmental variables during spring algal bloom in Xiangxi Bay of Three Gorges Reservoir, the support vector regression (SVR) model was established. In surveys, 11 stations have been investigated and 264 samples were collected weekly from March 4 to May 13 in 2007 and February 16 to May 10 in 2008. The parameters in SVR model were optimized by leave one out cross validation. The squared correlation coefficient

Cite this paper

H. Luo, D. Liu and Y. Huang, "Support Vector Regression Model of Chlorophyll-*a* during Spring Algal Bloom in Xiangxi Bay of Three Gorges Reservoir, China," *Journal of Environmental Protection*, Vol. 3 No. 5, 2012, pp. 420-425. doi: 10.4236/jep.2012.35052.

H. Luo, D. Liu and Y. Huang, "Support Vector Regression Model of Chlorophyll-

References

[1] S. R. Carpenter, D. Ludwig and W. A. Brock, “Management of eutrophication for Lakes Subject to Potentially Irreversible Change,” Ecological Applications, Vol. 9, 1999, pp. 751-771. doi:10.1890/1051-0761(1999)009[0751:MOEFLS]2.0.CO;2

[2] D. W. Schindler, “Recent Advances in the Understanding and Management of Eutrophication,” Limnology and Oceanography, Vol. 51, 2006, pp. 356-363. doi:10.4319/lo.2006.51.1_part_2.0356

[3] I. Kagalou, E. Papastergiadou and I. Leonardos, “Long Term Changes in the Eutrophication Process in a Shallow Mediterranean Lake Ecosystem of W. Greece: Response after the Reduction of External Load,” Journal of Environmental Management, Vol. 87, No. 3, 2008, pp. 497-506. doi:10.1016/j.jenvman.2007.01.039

[4] Jr H. B. Glasgow and J. M. Burkholder, “Water Quality Trends and Management Implications from a Five-Year Study of a Eutrophic Estuary,” Ecological Applications, Vol. 10, 2000, pp. 1024-1046. doi:10.1890/1051-0761(2000)010[1024:WQTAMI]2.0.CO;2

[5] P. J. Dillon and F. H. Rigler, “The Phosphorus-Chlorophyll Relationship in Lakes,” Limnology and Oceanography, Vol. 19, No. 5, 1974, pp. 767-773. doi:10.4319/lo.1974.19.5.0767

[6] K. An and S. S. Park, “Indirect Influence of the Summer Monsoon on Chlorophyll-Total Phosphorus Models in Reservoirs: A Case Study,” Ecological Modelling, Vol. 152, No. 2-3, 2002, pp. 191-203. doi:10.1016/S0304-3800(02)00020-0

[7] G. Phillips, O. P. Pietilainen, L. Carvalho, A. Solimini, A. L. Solheim and A. Cardoso, “Chlorophyll–Nutrient Relationships of Different Lake Types Using a Large European Dataset,” Aquatic Ecology, Vol. 42, No. 2, 2008, pp. 213-226. doi:10.1007/s10452-008-9180-0

[8] L. Hakanson, J. M. Malmaeus, U. Bodemer and V. Gerhardt, “Coefficients of Variation for Chlorophyll, Green Algae, Diatoms, Cryptophytes And Blue-Greens in Rivers as a Basis For Predictive Modelling and Aquatic Management,” Ecological Modelling, Vol. 169, No. 1, 2003, pp. 179-196. doi:10.1016/S0304-3800(03)00269-2

[9] K. K. Balachandran, K. V. Jayalakshmy, C. M. Laluraj, M. Nair, T. Joseph and P. Sheeba, “Step-Up Multiple Regression Model to Compute Chlorophyll a in the Coastal Waters off Cochin, Southwest Coast of India,” Environmental Monitoring and Assessment, Vol. 139, No. 1-3, 2008, pp. 217-226. doi:10.1007/s10661-007-9829-5

[10] N. K. Sharma, D. Mohan and A. K. Rai, “Predicting Phytoplankton Growth and Dynamics in Relation to Physico-Chemical Characteristics of Water Body,” Water Air & Soil Pollution, Vol. 202, No. 1-4, 2009, pp. 325-333. doi:10.1007/s11270-009-9979-x

[11] K. H. Cho, J.-H. Kang, S. J. Ki, Y. Park, S. M. Cha and J. H. Kim, “Determination of the optimal Parameters in Regression Models for the Prediction of Chlorophyll-a: A Case Study of the Yeongsan Reservoir, Korea,” Science of the Total Environment, Vol. 407, No. 8, 2009, pp. 2536-2545. doi:10.1016/j.scitotenv.2009.01.017

[12] Y. Liu, H. Guo and P. Yang, “Exploring the Influence of Lake Water Chemistry on Chlorophyll-a: A Multivariate Statistical Model Analysis,” Ecological Modelling, Vol. 221, No. 4, 2010, pp. 681-688. doi:10.1016/j.ecolmodel.2009.03.010

[13] M. Xu, G. M. Zeng, X. Y. Xu and G. H. Huang, “Application of Bayesian Regularized BP Neural Network Model for Analysis of Aquatic Ecological Data—A Case Study of Chlorophyll a Prediction in Nanzui Water Area of Dongting Lake,” Journal of Environmental Science, Vol. 17, No. 6, 2005, pp. 946-952.

[14] K.-S. Jeong, D.-K. Kim and G.-J. Joo, “River Phytoplankton Prediction Model by Artificial Neural Network: Model Performance and Selection of Input Variables to Predict Time-Series Phytoplankton Proliferations in a Regulated River System,” Ecological Informatics, Vol. 1, No. 3, 2006, pp. 235-245. doi:10.1016/j.ecoinf.2006.04.001

[15] V. Vapnik, “Statistical Learning Theory,” Wiley, New York, 1998.

[16] V. Vapnik, “An Overview of Statistical Learning Theory,” IEEE Transactions on Neural Networks, Vol. 10, No. 5, 1999, pp. 988-999. doi:10.1109/72.788640

[17] U. Thissen, R. van Brakel, A. P. de Weijer, W. J. Melssen and L. M. C. Buydens, “Using Support Vector Machines for Time Series Prediction,” Chemometrics and Intelligent Laboratory Systems, Vol. 69, No. 1-2, 2003, pp. 35-49. doi:10.1016/S0169-7439(03)00111-4

[18] J. G. Wu, J. H. Huang, X. G. Han, Z. Q. Xie and X. M. Gao, “Three-Gorge Dam—Experiment in Habitat Fragmentation?” Science, Vol. 300, No. 5623, 2003, pp. 1239-1240. doi:10.1126/science.1083312

[19] L. Ye, D. F. Li, T. Tang, X. D. Qu and Q. H. Cai, “Spatial Distribution of Water Quality in Xiangxi River, China,” China Journal of Applied Ecology, Vol. 14, No. 11, 2003, pp. 1959-1962.

[20] T. Tang, D. F. Li, W. B. Pan, X. D. Qu and Q. H. Cai, “River Continuum Characteristics of Xiangxi River,” China Journal of Applied Ecology, Vol. 15, No. 1, 2004, pp. 141-144.

[21] H. Y. Wang, “Effects of the Three Gorges Reservoir on the Water Environment of the Xiangxi River with the Proposal of Countermeasures,” Resource and Environment of Yangtze Basin, Vol. 14, No. 2, 2005, pp. 233-237.

[22] X. C. Jin and Q. Y. Tu, “Criterion of Eutrophication Survey on Lakes,” 2nd Edition, Environmental Science Press, Beijing, 1990.

[23] A. J. Lewitus, E. T. Koepfler and J. T. Morris, “Seasonal Variation in the Regulation of Phytoplankton by Nitrogen and Grazing in a Salt Marsh Estuary,” Limnology and Oceanography, Vol. 43, No. 4, 1998, pp. 636-646. doi:10.4319/lo.1998.43.4.0636

[24] S. R. Gunn, “Support Vector Machines for Classification and Regression,” Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton, Southampton, 1997. http://www.isis.ecs.soton.ac.uk/isystems/kernel/

[1] S. R. Carpenter, D. Ludwig and W. A. Brock, “Management of eutrophication for Lakes Subject to Potentially Irreversible Change,” Ecological Applications, Vol. 9, 1999, pp. 751-771. doi:10.1890/1051-0761(1999)009[0751:MOEFLS]2.0.CO;2

[2] D. W. Schindler, “Recent Advances in the Understanding and Management of Eutrophication,” Limnology and Oceanography, Vol. 51, 2006, pp. 356-363. doi:10.4319/lo.2006.51.1_part_2.0356

[3] I. Kagalou, E. Papastergiadou and I. Leonardos, “Long Term Changes in the Eutrophication Process in a Shallow Mediterranean Lake Ecosystem of W. Greece: Response after the Reduction of External Load,” Journal of Environmental Management, Vol. 87, No. 3, 2008, pp. 497-506. doi:10.1016/j.jenvman.2007.01.039

[4] Jr H. B. Glasgow and J. M. Burkholder, “Water Quality Trends and Management Implications from a Five-Year Study of a Eutrophic Estuary,” Ecological Applications, Vol. 10, 2000, pp. 1024-1046. doi:10.1890/1051-0761(2000)010[1024:WQTAMI]2.0.CO;2

[5] P. J. Dillon and F. H. Rigler, “The Phosphorus-Chlorophyll Relationship in Lakes,” Limnology and Oceanography, Vol. 19, No. 5, 1974, pp. 767-773. doi:10.4319/lo.1974.19.5.0767

[6] K. An and S. S. Park, “Indirect Influence of the Summer Monsoon on Chlorophyll-Total Phosphorus Models in Reservoirs: A Case Study,” Ecological Modelling, Vol. 152, No. 2-3, 2002, pp. 191-203. doi:10.1016/S0304-3800(02)00020-0

[7] G. Phillips, O. P. Pietilainen, L. Carvalho, A. Solimini, A. L. Solheim and A. Cardoso, “Chlorophyll–Nutrient Relationships of Different Lake Types Using a Large European Dataset,” Aquatic Ecology, Vol. 42, No. 2, 2008, pp. 213-226. doi:10.1007/s10452-008-9180-0

[8] L. Hakanson, J. M. Malmaeus, U. Bodemer and V. Gerhardt, “Coefficients of Variation for Chlorophyll, Green Algae, Diatoms, Cryptophytes And Blue-Greens in Rivers as a Basis For Predictive Modelling and Aquatic Management,” Ecological Modelling, Vol. 169, No. 1, 2003, pp. 179-196. doi:10.1016/S0304-3800(03)00269-2

[9] K. K. Balachandran, K. V. Jayalakshmy, C. M. Laluraj, M. Nair, T. Joseph and P. Sheeba, “Step-Up Multiple Regression Model to Compute Chlorophyll a in the Coastal Waters off Cochin, Southwest Coast of India,” Environmental Monitoring and Assessment, Vol. 139, No. 1-3, 2008, pp. 217-226. doi:10.1007/s10661-007-9829-5

[10] N. K. Sharma, D. Mohan and A. K. Rai, “Predicting Phytoplankton Growth and Dynamics in Relation to Physico-Chemical Characteristics of Water Body,” Water Air & Soil Pollution, Vol. 202, No. 1-4, 2009, pp. 325-333. doi:10.1007/s11270-009-9979-x

[11] K. H. Cho, J.-H. Kang, S. J. Ki, Y. Park, S. M. Cha and J. H. Kim, “Determination of the optimal Parameters in Regression Models for the Prediction of Chlorophyll-a: A Case Study of the Yeongsan Reservoir, Korea,” Science of the Total Environment, Vol. 407, No. 8, 2009, pp. 2536-2545. doi:10.1016/j.scitotenv.2009.01.017

[12] Y. Liu, H. Guo and P. Yang, “Exploring the Influence of Lake Water Chemistry on Chlorophyll-a: A Multivariate Statistical Model Analysis,” Ecological Modelling, Vol. 221, No. 4, 2010, pp. 681-688. doi:10.1016/j.ecolmodel.2009.03.010

[13] M. Xu, G. M. Zeng, X. Y. Xu and G. H. Huang, “Application of Bayesian Regularized BP Neural Network Model for Analysis of Aquatic Ecological Data—A Case Study of Chlorophyll a Prediction in Nanzui Water Area of Dongting Lake,” Journal of Environmental Science, Vol. 17, No. 6, 2005, pp. 946-952.

[14] K.-S. Jeong, D.-K. Kim and G.-J. Joo, “River Phytoplankton Prediction Model by Artificial Neural Network: Model Performance and Selection of Input Variables to Predict Time-Series Phytoplankton Proliferations in a Regulated River System,” Ecological Informatics, Vol. 1, No. 3, 2006, pp. 235-245. doi:10.1016/j.ecoinf.2006.04.001

[15] V. Vapnik, “Statistical Learning Theory,” Wiley, New York, 1998.

[16] V. Vapnik, “An Overview of Statistical Learning Theory,” IEEE Transactions on Neural Networks, Vol. 10, No. 5, 1999, pp. 988-999. doi:10.1109/72.788640

[17] U. Thissen, R. van Brakel, A. P. de Weijer, W. J. Melssen and L. M. C. Buydens, “Using Support Vector Machines for Time Series Prediction,” Chemometrics and Intelligent Laboratory Systems, Vol. 69, No. 1-2, 2003, pp. 35-49. doi:10.1016/S0169-7439(03)00111-4

[18] J. G. Wu, J. H. Huang, X. G. Han, Z. Q. Xie and X. M. Gao, “Three-Gorge Dam—Experiment in Habitat Fragmentation?” Science, Vol. 300, No. 5623, 2003, pp. 1239-1240. doi:10.1126/science.1083312

[19] L. Ye, D. F. Li, T. Tang, X. D. Qu and Q. H. Cai, “Spatial Distribution of Water Quality in Xiangxi River, China,” China Journal of Applied Ecology, Vol. 14, No. 11, 2003, pp. 1959-1962.

[20] T. Tang, D. F. Li, W. B. Pan, X. D. Qu and Q. H. Cai, “River Continuum Characteristics of Xiangxi River,” China Journal of Applied Ecology, Vol. 15, No. 1, 2004, pp. 141-144.

[21] H. Y. Wang, “Effects of the Three Gorges Reservoir on the Water Environment of the Xiangxi River with the Proposal of Countermeasures,” Resource and Environment of Yangtze Basin, Vol. 14, No. 2, 2005, pp. 233-237.

[22] X. C. Jin and Q. Y. Tu, “Criterion of Eutrophication Survey on Lakes,” 2nd Edition, Environmental Science Press, Beijing, 1990.

[23] A. J. Lewitus, E. T. Koepfler and J. T. Morris, “Seasonal Variation in the Regulation of Phytoplankton by Nitrogen and Grazing in a Salt Marsh Estuary,” Limnology and Oceanography, Vol. 43, No. 4, 1998, pp. 636-646. doi:10.4319/lo.1998.43.4.0636

[24] S. R. Gunn, “Support Vector Machines for Classification and Regression,” Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton, Southampton, 1997. http://www.isis.ecs.soton.ac.uk/isystems/kernel/