TITLE:
Analysis of Ammonia Nitrogen Content in Water Based on Weighted Least Squares Support Vector Machine (WLSSVM) Algorithm
AUTHORS:
Jinwu Ju, Lanying Wang
KEYWORDS:
Support Vector Machine, Water Quality, Ammonia Nitrogen, Forecasting Model
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.9 No.2,
February
18,
2016
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.