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

Abstract

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.

Surrogate-Based Uncertainty Analysis for Groundwater Contaminant Transport in a Chromium Residue Site Located in Southern China

“…In recent years, several surrogate model approaches have been suggested to approximate simulation models for uncertainty analysis, such as Polynomial Chaos Expansion (PCE); Gaussian Process Emulation (GPE) [41]; Gaussian processes [42]; Kriging [26,43,44]; Radial Basis Function (RBF) [45]; Support Vector Regression (SVR) [46]; Artificial Neural Network (ANN) [47]; Multi-gene Genetic programming (MGGP) [48]; Kernel Extreme Learning Machine (KELM) [49]; a hybrid approach using the Multilevel Monte Carlo method (MLMC); a graph convolutional neural network and a feed-forward neural network [50]; and a Deep Belief Neural Network (DBNN) [51]. While earlier studies have demonstrated certain achievements, the application of surrogate models encounters challenges related to scalability and accuracy, particularly in scenarios where contaminantrelated associations exhibits pronounced non-linearity or high dimensionality [52].…”

Investigation on the Hydrogeochemical Characteristics and Controlling Mechanisms of Groundwater in the Coastal Aquifer

“…Furthermore, Piper diagrams, Gibbs plots, ion ratios, and saturation indices play a significant role in studying the hydrogeochemical controlling mechanisms [35][36][37][38][39][40]. In addition, numerical simulation methods can be used to evaluate the exchange of groundwater with seawater and the transport characteristics of nutrients [41][42][43][44]. Water quality indices (WQIs), machine learning, and the partial least squares regression model (PLSR) are typically used to evaluate the pollution level in groundwater [45][46][47].…”

Experimental and Computational Analysis for Optimization of Seawater Biodegradability Using Photo Catalysis

“…A fuzzy system is the first stage in building a network of fuzzy systems and can be created by "if-then" rules. The mathematical techniques are known as the recurrent network (RN), time-lagged recurrent network (TLRN), ANN, and ANFIS are directly derived from the workings of the human brain [30]. These methods are promising for simulating response variables and can also be used with nonlinear systems due to their simplicity.…”