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A Study of Real-Time Forecasting for the Urban Lake-Groundwater Coupled System Using Surrogate Models

Authors :
Liu Chuankun
Hu Yue
Yu Ting
Xu Qiang
Liu Chaoqing
An Quan
Shen Chao
Source :
E3S Web of Conferences, Vol 118, p 03018 (2019)
Publication Year :
2019
Publisher :
EDP Sciences, 2019.

Abstract

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.

Subjects

Subjects :
Environmental sciences
GE1-350

Details

Language :
English, French
ISSN :
22671242
Volume :
118
Database :
Directory of Open Access Journals
Journal :
E3S Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.305a0c3b838f4d1b99989960bf07ac06
Document Type :
article
Full Text :
https://doi.org/10.1051/e3sconf/201911803018