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Water 2015, 7(4), 1610-1627; doi:10.3390/w7041610

A Structurally Simplified Hybrid Model of Genetic Algorithm and Support Vector Machine for Prediction of Chlorophyll a in Reservoirs

1
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
2
Key Laboratory for Water and Sediment Sciences of Ministry of Education, School of Environment, Beijing Normal University, Beijing 100875, China
3
Chinese Academy for Environmental Planning, Ministry of Environmental Protection, Beijing 100012, China
4
Management Office of Miyun Reservoir, Beijing 101512, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lutz Breuer and Philipp Kraft
Received: 13 January 2015 / Revised: 21 March 2015 / Accepted: 3 April 2015 / Published: 16 April 2015
(This article belongs to the Special Issue Hydro-Ecological Modeling)
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Abstract

With decreasing water availability as a result of climate change and human activities, analysis of the influential factors and variation trends of chlorophyll a has become important to prevent reservoir eutrophication and ensure water supply safety. In this paper, a structurally simplified hybrid model of the genetic algorithm (GA) and the support vector machine (SVM) was developed for the prediction of monthly concentration of chlorophyll a in the Miyun Reservoir of northern China over the period from 2000 to 2010. Based on the influence factor analysis, the four most relevant influence factors of chlorophyll a (i.e., total phosphorus, total nitrogen, permanganate index, and reservoir storage) were extracted using the method of feature selection with the GA, which simplified the model structure, making it more practical and efficient for environmental management. The results showed that the developed simplified GA-SVM model could solve nonlinear problems of complex system, and was suitable for the simulation and prediction of chlorophyll a with better performance in accuracy and efficiency in the Miyun Reservoir. View Full-Text
Keywords: concentration prediction; chlorophyll a; support vector machine; genetic algorithm; Miyun Reservoir concentration prediction; chlorophyll a; support vector machine; genetic algorithm; Miyun Reservoir
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Su, J.; Wang, X.; Zhao, S.; Chen, B.; Li, C.; Yang, Z. A Structurally Simplified Hybrid Model of Genetic Algorithm and Support Vector Machine for Prediction of Chlorophyll a in Reservoirs. Water 2015, 7, 1610-1627.

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