Journal of Software Engineering and Applications

Volume 9, Issue 2 (February 2016)

ISSN Print: 1945-3116   ISSN Online: 1945-3124

Google-based Impact Factor: 2  Citations  

Analysis of Ammonia Nitrogen Content in Water Based on Weighted Least Squares Support Vector Machine (WLSSVM) Algorithm

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DOI: 10.4236/jsea.2016.92002    4,275 Downloads   5,111 Views  Citations
Author(s)

ABSTRACT

Determination of ammonia nitrogen content in water is the basic item of the environmental water pollution, and is the key index to evaluate the water quality. This article designs a water quality monitoring system based on the on-line automatic ammonia nitrogen monitoring system, and establishes a forecasting model based on the weighted least squares support vector machine algorithm. The weighted least squares support vector machine algorithm increases the weight parameter setting, improves the speed and accuracy of prediction learning, and improves the robustness. In this article, a comparison between neural network model and weighted least square support vector machine model is made, which shows that the weighted least squares support vector machine model has better prediction accuracy.

Share and Cite:

Ju, J. and Wang, L. (2016) Analysis of Ammonia Nitrogen Content in Water Based on Weighted Least Squares Support Vector Machine (WLSSVM) Algorithm. Journal of Software Engineering and Applications, 9, 45-51. doi: 10.4236/jsea.2016.92002.

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