Next Article in Journal
Effectiveness of Biomass/Abundance Comparison (ABC) Models in Assessing the Response of Hyporheic Assemblages to Ammonium Contamination
Next Article in Special Issue
Site Investigation and Remediation of Sulfate-Contaminated Groundwater Using Integrated Hydraulic Capture Techniques
Previous Article in Journal
An Assessment of Water Supply Governance in Armed Conflict Areas of Rakhine State, Myanmar
Previous Article in Special Issue
Hydrochemical Characteristics and Hydrogeochemical Simulation Research of Groundwater in the Guohe River Basin (Henan Section)
 
 
Article

Uncertainty Analysis of Numerical Simulation of Seawater Intrusion Using Deep Learning-Based Surrogate Model

1
Song-Liao River Water Resources Commission, Changchun 130000, China
2
River Basin Planning & Policy Research Center of Song-Liao River Water Resources Commission, Changchun 130000, China
*
Author to whom correspondence should be addressed.
Academic Editors: Nianqing Zhou, Simin Jiang and Xihua Wang
Water 2022, 14(18), 2933; https://doi.org/10.3390/w14182933
Received: 6 August 2022 / Revised: 13 September 2022 / Accepted: 15 September 2022 / Published: 19 September 2022
Seawater intrusion is expected to cause a shortage of freshwater resources in coastal areas which will hinder regional economic and social development. The consequences of global climate change include rising sea levels, which also affect the results of the predictions of seawater intrusion that are based on simulations. It is thus important to examine the impact of the randomness in the rise in sea levels on the uncertainty in the results of numerical simulations that are used to predict seawater intrusion. Deep learning has lately emerged as a popular area of research that has been used to establish surrogate models in this context. In this study, the authors have used deep learning to determine the complex and nonlinear mapping relationship between the inputs and outputs of a three-dimensional variable-density numerical model of seawater intrusion in the case of a limited number of training samples, wherein, this has improved the accuracy of the approximation of the surrogate models. We used the rise in sea level as a random variable, and then applied the Monte Carlo method to analyze the influence of randomness on the uncertainty in the results of the numerical predictions of seawater intrusion. Statistical analyses and interval estimations of the Cl concentration and the area of seawater intrusion were conducted at typical observation wells. The work that is here provides a reliable reference for decision making in the area. View Full-Text
Keywords: artificial intelligence; seawater intrusion; deep learning; surrogate model; uncertainty analysis artificial intelligence; seawater intrusion; deep learning; surrogate model; uncertainty analysis
Show Figures

Figure 1

MDPI and ACS Style

Miao, T.; Huang, H.; Guo, J.; Li, G.; Zhang, Y.; Chen, N. Uncertainty Analysis of Numerical Simulation of Seawater Intrusion Using Deep Learning-Based Surrogate Model. Water 2022, 14, 2933. https://doi.org/10.3390/w14182933

AMA Style

Miao T, Huang H, Guo J, Li G, Zhang Y, Chen N. Uncertainty Analysis of Numerical Simulation of Seawater Intrusion Using Deep Learning-Based Surrogate Model. Water. 2022; 14(18):2933. https://doi.org/10.3390/w14182933

Chicago/Turabian Style

Miao, Tiansheng, He Huang, Jiayuan Guo, Guanghua Li, Yu Zhang, and Naijia Chen. 2022. "Uncertainty Analysis of Numerical Simulation of Seawater Intrusion Using Deep Learning-Based Surrogate Model" Water 14, no. 18: 2933. https://doi.org/10.3390/w14182933

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop