Multi-Classification Method for Determining Coastal Water Quality based on SVM with Grid Search and KNN

doi:10.23940/ijpe.19.10.p7.26182627

Abstract

Abstract:

To address the problem of multi-classification of coastal water quality, this work envisioned the establishment of a multi-classification model of coastal water quality that uses an improved support vector machine. Inorganic nitrogen, active phosphate, chemical oxygen demand, pH, and dissolved oxygen were the input parameters of the model. The parameters of the support vector machine (SVM) model were optimized by cross-validation and the grid search optimization method, and the optimal parameters of the classifier were obtained. Subsequently, the KNN method was combined, and the optimized model was used to classify the water quality. The optimal parameters for the classifier were finally obtained. The experimental results showed that compared with SVM before optimization, the accuracy of the optimized model was improved by up to 10%, and the sample size was less.

Key words: SVM, grid search, KNN, coastal water quality

Guoqiang Xie, Yi Zhao, Shiyi Xie, Miaofen Huang, and Ying Zhang. Multi-Classification Method for Determining Coastal Water Quality based on SVM with Grid Search and KNN [J]. Int J Performability Eng, 2019, 15(10): 2618-2627.

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