scholarly journals A surrogate-based simulation–optimization approach for coastal aquifer management

2020 ◽  
Vol 20 (8) ◽  
pp. 3404-3418
Author(s):  
Zheng Han ◽  
Wenxi Lu ◽  
Yue Fan ◽  
Jin Lin ◽  
Qian Yuan
Keyword(s):  
Simulation Model ◽  
Groundwater Management ◽  
Optimization Model ◽  
Surrogate Model ◽  
Simulation Optimization ◽  
Surrogate Models ◽  
Variable Density ◽  
Support Vector ◽  
Aquifer Management ◽  
Groundwater Simulation

Abstract This study proposed a pumping-injection (P-I) groundwater management strategy based on a simulation–optimization (S-O) framework to mitigate seawater intrusion (SI). The methodology was applied to a real case in Longkou, China. A three-dimensional variable-density groundwater simulation model was established to simulate and predict the SI process. In the S-O framework, while solving the optimization model, it is required to call the simulation model thousands of times, which leads to enormous computational load. In this case, the Kriging and support vector regression (SVR) surrogate models were established for the simulation model respectively. Furthermore, the ensemble surrogate modeling technique was applied to construct the Kriging-SVR ensemble surrogate model. The most accurate surrogate model was selected as the substitute for the simulation model, saving considerable computing costs. The results show that the ensemble surrogate model performs better than the stand-alone surrogate models in accuracy, indicating that combining stand-alone surrogate models is a potential modeling method for the surrogate model of the variable-density groundwater simulation model. By solving the optimization model, the optimal pumping and injection schemes under different scenarios were obtained. The optimization results demonstrate that the proposed methodology is effective and stable in coastal groundwater management.

scholarly journals Uncertainty Analysis in the Optimization Process of Groundwater Exploitation Scheme Based on SVR Method—A Case Study of Hetao Plain

2018 ◽  
Author(s):  
Yongkai An ◽  
Wenxi Lu ◽  
Xueman Yan
Keyword(s):  
Uncertainty Analysis ◽  
Simulation Model ◽  
Optimization Model ◽  
Surrogate Model ◽  
Surrogate Models ◽  
Groundwater Table ◽  
Pumping Rate ◽  
Groundwater Exploitation ◽  
Computational Load ◽  
Hetao Plain

This paper introduces a surrogate model to reduce the huge computational load in the process of simulation-optimization and uncertainty analysis. First, the groundwater numerical simulation model was established, calibrated and verified in the northeast of Hetao Plain. Second, two surrogate models of simulation model were established using support vector regression (SVR) method, one (surrogate model A, SMA) was used to describe the corresponding relationship between the pumping rate and average groundwater table drawdown, and another (surrogate model B, SMB) was used to express the corresponding relationship between the hydrogeological parameter values and average groundwater table drawdown. Third, an optimization model was established to search an optimal groundwater exploitation scheme using the maximum total pumping rate as objective function and the limitative average groundwater table drawdown as constraint condition, the SMA was invoked by the optimization model for obtaining the optimal groundwater exploitation scheme. Finally, the SMB was invoked in the process of uncertainty analysis for assessing the reliability of optimal groundwater exploitation scheme. Results show that the relative error and root mean square error between simulation model and the two surrogate models are both less than 5%, which is a high approximation accuracy. The SVR surrogate model developed in this study could not only considerably reduce the computational load, but also maintain high computational accuracy. The optimal total pumping rate is 7947 m3/d and the reliability of optimal scheme is 40.21%. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme and assessing the reliability of scheme quickly and accurately.

scholarly journals Surrogate-based pumping optimization of coastal aquifers under limited computational budgets

2017 ◽  
Vol 20 (1) ◽  
pp. 164-176 ◽  
Author(s):  
Vasileios Christelis ◽  
Rommel G. Regis ◽  
Aristotelis Mantoglou
Keyword(s):  
Coastal Aquifers ◽  
Surrogate Model ◽  
Coastal Aquifer ◽  
Surrogate Models ◽  
Salt Transport ◽  
Direct Optimization ◽  
Aquifer Management ◽  
Pumping Optimization ◽  
Local Improvement ◽  
Coastal Aquifer Management

Abstract The computationally expensive variable density and salt transport numerical models hinder the implementation of simulation-optimization routines for coastal aquifer management. To reduce the computational cost, surrogate models have been utilized in pumping optimization of coastal aquifers. However, it has not been previously addressed whether surrogate modelling is effective given a limited number of numerical simulations with the seawater intrusion model. To that end, two surrogate-based optimization (SBO) frameworks are employed and compared against the direct optimization approach, under restricted computational budgets. The first, a surrogate-assisted algorithm, employs a strategy which aims at a fast local improvement of the surrogate model around optimal values. The other, balances global and local improvement of the surrogate model and is applied for the first time in coastal aquifer management. The performance of the algorithms is investigated for optimization problems of moderate and large dimensionalities. The statistical analysis indicates that for the specified computational budgets, the sample means of the SBO methods are statistically significantly better than those of the direct optimization. Additionally, the selection of cubic radial basis functions as surrogate models, enables the construction of very fast approximations for problems with up to 40 decision variables and 40 constraint functions.

scholarly journals Rapidly Tuning the PID Controller Based on the Regional Surrogate Model Technique in the UAV Formation

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 527
Author(s):  
Binglin Wang ◽  
Xiaojun Duan ◽  
Liang Yan ◽  
Juan Deng ◽  
Jiangtao Chen
Keyword(s):  
Simulation Model ◽  
Surrogate Model ◽  
Linear Quadratic Regulator ◽  
Surrogate Models ◽  
Singular Points ◽  
Pid Controllers ◽  
Linear Quadratic ◽  
Order Of Magnitude ◽  
Pareto Fronts ◽  
Model Technique

The leader–follower structure is widely used in unmanned aerial vehicle formation. This paper adopts the proportional-integral-derivative (PID) and the linear quadratic regulator controllers to construct the leader–follower formation. Tuning the PID controllers is generally empirical; hence, various surrogate models have been introduced to identify more refined parameters with relatively lower cost. However, the construction of surrogate models faces the problem that the singular points may affect the accuracy, such that the global surrogate models may be invalid. Thus, to tune controllers quickly and accurately, the regional surrogate model technique (RSMT), based on analyzing the regional information entropy, is proposed. The proposed RSMT cooperates only with the successful samples to mitigate the effect of singular points along with a classifier screening failed samples. Implementing the RSMT with various kinds of surrogate models, this study evaluates the Pareto fronts of the original simulation model and the RSMT to compare their effectiveness. The results show that the RSMT can accurately reconstruct the simulation model. Compared with the global surrogate models, the RSMT reduces the run time of tuning PID controllers by one order of magnitude, and it improves the accuracy of surrogate models by dozens of orders of magnitude.

scholarly journals Optimization of Irrigation Scheduling for Maize in an Arid Oasis Based on Simulation–Optimization Model

Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 935 ◽  
Author(s):  
Jiang Li ◽  
Xiyun Jiao ◽  
Hongzhe Jiang ◽  
Jian Song ◽  
Lina Chen
Keyword(s):  
Simulation Model ◽  
Optimization Model ◽  
Simulation Optimization ◽  
Water Productivity ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Water Saving ◽  
Scheduling Optimization ◽  
Agricultural Water Resources ◽  
Crop Water Consumption

In arid regions, irrigation scheduling optimization is efficient in coping with the shortage of agricultural water resources. This paper developed a simulation–optimization model for irrigation scheduling optimization for the main crop in an arid oasis, aiming to maximize crop yield and minimize crop water consumption. The model integrated the soil water balance simulation model and the optimization model for crop irrigation scheduling. The simulation model was firstly calibrated and validated based on field experiment data for maize in 2012 and 2013, respectively. Then, considering the distribution of soil types and irrigation districts in the study area, the model was used to solve the optimal irrigation schedules for the scenarios of status quo and typical climate years. The results indicated that the model is applicable for reflecting the complexities of simulation–optimization for maize irrigation scheduling. The optimization results showed that the irrigation water-saving potential of the study area was between 97 mm and 240 mm, and the average annual optimal yield of maize was over 7.3 t/ha. The simulation–optimization model of irrigation schedule established in this paper can provide a technical means for the formulation of irrigation schedules to ensure yield optimization and water productivity or water saving.

scholarly journals Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
George Kopsiaftis ◽  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Nikolaos Doulamis ◽  
Aristotelis Mantoglou
Keyword(s):  
Gaussian Process ◽  
Seawater Intrusion ◽  
Surrogate Model ◽  
Gaussian Process Regression ◽  
Variable Density ◽  
Regression Method ◽  
Bayesian Optimization ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Pumping Rate

Accurate prediction of the seawater intrusion extent is necessary for many applications, such as groundwater management or protection of coastal aquifers from water quality deterioration. However, most applications require a large number of simulations usually at the expense of prediction accuracy. In this study, the Gaussian process regression method is investigated as a potential surrogate model for the computationally expensive variable density model. Gaussian process regression is a nonparametric kernel-based probabilistic model able to handle complex relations between input and output. In this study, the extent of seawater intrusion is represented by the location of the 0.5 kg/m3 iso-chlore at the bottom of the aquifer (seawater intrusion toe). The initial position of the toe, expressed as the distance of the specific line from a number of observation points across the coastline, along with the pumping rates are the surrogate model inputs, whereas the final position of the toe constitutes the output variable set. The training sample of the surrogate model consists of 4000 variable density simulations, which differ not only in the pumping rate pattern but also in the initial concentration distribution. The Latin hypercube sampling method is used to obtain the pumping rate patterns. For comparison purposes, a number of widely used regression methods are employed, specifically regression trees and Support Vector Machine regression (linear and nonlinear). A Bayesian optimization method is applied to all the regressors, to maximize their efficiency in the prediction of seawater intrusion. The final results indicate that the Gaussian process regression method, albeit more time consuming, proved to be more efficient in terms of the mean absolute error (MAE), the root mean square error (RMSE), and the coefficient of determination (R2).

Optimization of Denser Nonaqueous Phase Liquids-contaminated groundwater remediation based on Kriging surrogate model

2013 ◽  
Vol 8 (2) ◽  
pp. 304-314 ◽  
Author(s):  
Wenxi Lu ◽  
Haibo Chu ◽  
Ying Zhao ◽  
Jiannan Luo
Keyword(s):  
Simulation Model ◽  
Optimization Model ◽  
Surrogate Model ◽  
Groundwater Remediation ◽  
Groundwater Resources ◽  
Optimal Strategies ◽  
Nonaqueous Phase Liquids ◽  
Kriging Surrogate Model ◽  
Aquifer Remediation ◽  
Remediation Process

Spillage of large amounts of Denser Nonaqueous Phase Liquids (DNAPLs) had resulted in serious pollution of groundwater resources throughout the world; a large number of studies had demonstrated surfactant-enhanced remediation is a more effective approach to remediate DNAPLs contaminations. In this paper, the remediation optimization process was carried out in three steps. Firstly, a water-oil-surfactant simulation model had been firstly established to simulate a surfactant enhanced aquifer remediation process. The Kriging surrogate model had been developed to get a similar input–output relationship with simulation model. In the final, a nonlinear optimization model was formulated for the minimum cost, and Kriging surrogate model had been embedded into the optimization model as a constrained condition. What is more, simulated annealing method was used to solve the optimization model and give the optimal Surfactant-Enhanced Aquifer Remediation strategy. The results showed Kriging surrogate model had reduced computational burden and make the optimization model easy to solve, and the optimal strategies gave an effective guide to contaminants remediation process.

scholarly journals Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India

2020 ◽  
Vol 17 (10) ◽  
pp. 3521
Author(s):  
Madan K. Jha ◽  
Richard C. Peralta ◽  
Sasmita Sahoo
Keyword(s):  
Water Resources ◽  
Simulation Model ◽  
Seawater Intrusion ◽  
Optimization Model ◽  
Crop Yields ◽  
Simulation Optimization ◽  
Optimization Models ◽  
Water Model ◽  
Cropping Pattern ◽  
Groundwater Exploitation

Water resources sustainability is a worldwide concern because of climate variability, growing population, and excessive groundwater exploitation in order to meet freshwater demand. Addressing these conflicting challenges sometimes can be aided by using both simulation and mathematical optimization tools. This study combines a groundwater-flow simulation model and two optimization models to develop optimal reconnaissance-level water management strategies. For a given set of hydrologic and management constraints, both of the optimization models are applied to part of the Mahanadi River basin groundwater system, which is an important source of water supply in Odisha State, India. The first optimization model employs a calibrated groundwater simulation model (MODFLOW-2005, the U.S. Geological Survey modular ground-water model) within the Simulation-Optimization MOdeling System (SOMOS) module number 1 (SOMO1) to estimate maximum permissible groundwater extraction, subject to suitable constraints that protect the aquifer from seawater intrusion. The second optimization model uses linear programming optimization to: (a) optimize conjunctive allocation of surface water and groundwater and (b) to determine a cropping pattern that maximizes net annual returns from crop yields, without causing seawater intrusion. Together, the optimization models consider the weather seasons, and the suitability and variability of existing cultivable land, crops, and the hydrogeologic system better than the models that do not employ the distributed maximum groundwater pumping rates that will not induce seawater intrusion. The optimization outcomes suggest that minimizing agricultural rice cultivation (especially during the non-monsoon season) and increasing crop diversification would improve farmers’ livelihoods and aid sustainable use of water resources.

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