A wavelet-based autoregressive fuzzy model for forecasting algal blooms
A wavelet-based autoregressive fuzzy model for forecasting algal blooms
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DOI：
10.1016/j.envsoft.2014.08.014
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Yeesock Kim;Hyun-suk Shin;J. Plummer
中科院分区：
文献类型：
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作者：
Yeesock Kim;Hyun-suk Shin;J. Plummer
本文提出了一种预测藻华复杂行为的模糊模型。这些模型是通过集成自回归模型、Takagi-Sugeno模糊模型和离散小波变换算法来开发的。利用减法聚类技术确定模型的前提部分，并利用加权最小二乘法对结果部分进行优化。为了训练和验证所提出的模糊模型，从韩国金河大冲水库收集了大量的数据集。这些数据既包括水质变量，也包括水文变量。以总氮、总磷、溶解氧、化学需氧量、生化需氧量、pH、气温、水温和出水为输入信号，以叶绿素a为输出信号。仿真结果表明，所提出的模糊模型对藻类水华预报是有效的。
This paper proposes fuzzy models for forecasting the complex behavior of algal blooms. The models are developed through the integration of autoregressive models, the Takagi-Sugeno fuzzy model, and discrete wavelet transform algorithms. The premise parts of the proposed models are determined using the subtractive clustering technique and the consequent parts are optimized using weighted least squares. To train and validate the proposed fuzzy models, a large number of data sets were collected from Daecheong reservoir in Geum River in the Republic of Korea. The data include both water quality and hydrological variables. Total nitrogen, total phosphorous, dissolved oxygen, chemical oxygen demand, biochemical oxygen demand,pH, air temperature, water temperature and outflow water were evaluated as input signals while chlorophyll-a was used as an output. It is demonstrated from the simulation that the proposed fuzzy models are effective in forecasting algal blooms.