Study on Prediction of Saltwater Intrusion Based on Support Vector Machine in the Pearl River Estuary
Abstract: Effected by multiple factors such as upstream runoff, tide, topography, and wind, the saltwater in-trusion of Pearl River Estuary displays extreme complexity. In this paper, a hybrid method based on Support Vector Machine (SVR) and Classification (SVC) is used to build a daily average salinity prediction model in the Pearl River Estuary which has considered the variation of upstream runoff and tide. The result shows that the model for saltwater intrusion prediction has high accuracy and only requires a few samples. Furthermore, the model has a good ability of self-learning and asso-ciative memory which can well simulate the complex saltwater intrusions of this estuary. The findings of this study also show that the previous average three-day runoff from upstream and the previous three-day tidal range from outlet have a good time correlation with saltwater intrusion in this estuary.
文章引用: 柯华斌 , 刘丙军 , 章 文 , 陈仲策 (2015) 基于支持向量机的珠江河口咸潮上溯预测研究。 水资源研究， 4， 46-54. doi: 10.12677/JWRR.2015.41006
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