Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 18 April 2018 / Approved: 24 April 2018 / Online: 24 April 2018 (16:56:02 CEST)

How to cite: An, Y.; Lu, W.; Yan, X. Uncertainty Analysis in the Optimization Process of Groundwater Exploitation Scheme Based on SVR Method—A Case Study of Hetao Plain. Preprints 2018, 2018040317 (doi: 10.20944/preprints201804.0317.v1). An, Y.; Lu, W.; Yan, X. Uncertainty Analysis in the Optimization Process of Groundwater Exploitation Scheme Based on SVR Method—A Case Study of Hetao Plain. Preprints 2018, 2018040317 (doi: 10.20944/preprints201804.0317.v1).

Abstract

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.

Subject Areas

surrogate model; SVR; simulation-optimization; uncertainty analysis

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