Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing, China.

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ID: 60983
2017
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Ranked #9 of 76 articles by views in Journal of environmental sciences (China)

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Abstract
Water quality forecasting is an essential part of water resource management. Spatiotemporal variations of water quality and their inherent constraints make it very complex. This study explored a data-based method for short-term water quality forecasting. Prediction of water quality indicators including dissolved oxygen, chemical oxygen demand by KMnO and ammonia nitrogen using support vector machine was taken as inputs of the particle swarm algorithm based optimal wavelet neural network to forecast the whole status index of water quality. Gubeikou monitoring section of Miyun reservoir in Beijing, China was taken as the study case to examine effectiveness of this approach. The experiment results also revealed that the proposed model has advantages of stability and time reduction in comparison with other data-driven models including traditional BP neural network model, wavelet neural network model and Gradient Boosting Decision Tree model. It can be used as an effective approach to perform short-term comprehensive water quality prediction.
Reference Key
zhang2017developmentjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhang, Lei;Zou, Zhihong;Shan, Wei;
Journal Journal of environmental sciences (China)
Year 2017
DOI
S1001-0742(16)30874-9
URL
Keywords
support vector machine particle swarm optimization water quality forecasting wavelet neural network

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