文章基本信息

标题：A Study of Real-Time Forecasting for the Urban Lake-Groundwater Coupled System Using Surrogate Models
本地全文：下载
作者：Chuankun Liu ; Yue Hu ; Ting Yu 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2019
卷号：118
页码：1-5
DOI：10.1051/e3sconf/201911803018
出版社：EDP Sciences
摘要：The real-time forecasting of flooding event and pollution emergency has a significant impact on the robust of urban lake and groundwater coupled system. However, the traditional statistical based prediction method is too rough while numerical based method is very time-consuming. In this study, a framework integrating surface water-groundwater coupled numerical model and surrogate model for real-time forecasting was proposed. The Artificial Neural Network (ANN) algorithm was used to train the surrogate model. The performance of the surrogate model was assessed with the number of training samples and hidden neurons as variates. More training samples would help improve the performance of the surrogate model indicated with R square value getting close to 1. The complex ANN with more hidden neurons performed better than the simple networks in the condition of enough training samples, and complex network without enough supporting training samples would be inferior to the simple network.
其他摘要：The real-time forecasting of flooding event and pollution emergency has a significant impact on the robust of urban lake and groundwater coupled system. However, the traditional statistical based prediction method is too rough while numerical based method is very time-consuming. In this study, a framework integrating surface water-groundwater coupled numerical model and surrogate model for real-time forecasting was proposed. The Artificial Neural Network (ANN) algorithm was used to train the surrogate model. The performance of the surrogate model was assessed with the number of training samples and hidden neurons as variates. More training samples would help improve the performance of the surrogate model indicated with R square value getting close to 1. The complex ANN with more hidden neurons performed better than the simple networks in the condition of enough training samples, and complex network without enough supporting training samples would be inferior to the simple network.