Prediction of Chlorophyll-a content using hybrid model of least squares support vector regression and radial basis function neural networks

Eutrophication has become a serious environment problem in many parts of the world and Chlorophyll-a concentration is one of the important parameters for the characterization of water quality, which reflects the degree of eutrophication and algae content in the water body. So establishing a forecasting model to predict the chlorophyll-a concentration in evaluation of eutrophication become more urgent. In this paper, a hybrid model of least squares support vector regression optimized by improved particle swarm optimization and radial basis function neural networks (IPSO-LSSVR-RBFNN) was proposed, which effectively modifying the forecasting accuracy by extracting the useful information in the error term of the traditional methods. A real monthly dataset that collected from a typical reservoir in China during 2010-2012 and two public datasets were used to evaluate the performance of the proposed hybrid model. From the experiment results, we can see that the proposed model of IPSO-LSSVR-RBFNN achieve a higher accuracy rate compared with other models.

[1]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[2]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[3]  J. Suykens,et al.  Recurrent least squares support vector machines , 2000 .

[4]  J. Boaventura Cunha,et al.  Greenhouse air temperature predictive control using the particle swarm optimisation algorithm , 2005 .

[5]  Pei-Yi Hao,et al.  New support vector algorithms with parametric insensitive/margin model , 2010, Neural Networks.

[6]  Josette Garnier,et al.  Coupled biogeochemical cycles: eutrophication and hypoxia in temperate estuaries and coastal marine ecosystems , 2011 .

[7]  Junta de Regulación,et al.  " " > , 2007 .

[8]  A. Kalteh.  Wavelet Genetic Algorithm-Support Vector Regression (Wavelet GA-SVR) for Monthly Flow Forecasting , 2015, Water Resources Management.

[9]  Daoliang Li,et al.  Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization , 2013 .

[10]  Nicholas John Anderson,et al.  Predicting Epilimnetic Phosphorus Concentrations Using an Improved Diatom-Based Transfer Function and Its Application to Lake Eutrophication Management , 1996 .

[11]  M. M. Ali,et al.  Improved particle swarm algorithms for global optimization , 2008, Appl. Math. Comput..

[12]  Duo Xu,et al.  An Approach to Estimating Product Design Time Based on Fuzzy $\nu$-Support Vector Machine , 2007, IEEE Transactions on Neural Networks.

[13]  J. Ryther,et al.  Nitrogen, Phosphorus, and Eutrophication in the Coastal Marine Environment , 1971, Science.

[14]  Katsuaki Koike,et al.  Improved Accuracy of Chlorophyll-a Concentration Estimates from MODIS Imagery Using a Two-Band Ratio Algorithm and Geostatistics: As Applied to the Monitoring of Eutrophication Processes over Tien Yen Bay (Northern Vietnam) , 2013, Remote. Sens..

[15]  Yan Jiang,et al.  Improved particle swarm algorithm for hydrological parameter optimization , 2010, Appl. Math. Comput..

[16]  Daoliang Li,et al.  A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction , 2013, Math. Comput. Model..

[17]  Yong Liu,et al.  A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations. , 2015, The Science of the total environment.

[18]  Hong-Sen Yan,et al.  Method for Product Design Time Forecasting Based on Support Vector Regression with Probabilistic Constraints , 2015, Appl. Artif. Intell..

[19]  Mingzhe Liu,et al.  Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm , 2015, Neurocomputing.