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首页> 外文会议>IEEE International Conference on Computer-Aided Industrial Design & Conceptual Design;IEEE International Conference on Intelligent Computing and Integrated Systems;IEEE Joint International Computer Science and Information Technology Conference;IEEE International Conference on Emergency Management and Management Sciences >Prediction to chlorophyll-a concentration of impoundment process in Xiangxi Bay of Three Gorges Reservoir
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Prediction to chlorophyll-a concentration of impoundment process in Xiangxi Bay of Three Gorges Reservoir

机译：三峡库区湘西湾水体叶绿素a浓度的预测。

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The support vector machines (SVM) model was established to predict chlorophyll-a concentration of impoundment process in Xiangxi Bay of Three Gorges Reservoir. In surveys, 10 stations have been investigated and 191 samples were collected from September 25 to October 14 in 2007. Using stepwise multiple linear regression (MLR) method, six important environmental factors (water temperature, dissolved oxygen, pH, phosphate, total nitrogen and ammonium nitrogen) were selected as independent variables in SVM model. The optimal parameters of the SVM model was determined based on leave one out cross validation (LOOCV). For the LOOCV test, the cross validated squared correlation coefficient Q2 for optimal SVM was 0.7428. Compared with stepwise MLR model, the SVM model has more powerful predictive capacity with the squared correlation coefficient R2 of 0.8768 for the test set.
机译：建立了支持向量机（SVM）模型，以预测三峡水库湘西湾的水体叶绿素a浓度。在调查中，于2007年9月25日至10月14日对10个站点进行了调查，并收集了191个样本。使用逐步多元线性回归（MLR）方法，确定了六个重要的环境因素（水温，溶解氧，pH，磷酸盐，总氮和在SVM模型中选择铵态氮作为自变量。 SVM模型的最佳参数是根据留一法交叉验证（LOOCV）确定的。对于LOOCV测试，最佳SVM的交叉验证平方相关系数Q2为0.7428。与逐步MLR模型相比，SVM模型具有更强大的预测能力，测试集的平方相关系数R2为0.8768。

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