Self-optimizer data-mining method for aquifer level prediction

Omid Bozorg-Haddad,Mohammad Delpasand,Hugo A Loáiciga

doi:10.2166/ws.2019.204

Omid Bozorg-Haddad, Mohammad Delpasand + Show 1 more

Open Access

https://doi.org/10.2166/ws.2019.204

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Abstract

Abstract Groundwater management requires accurate methods for simulating and predicting groundwater processes. Data-based methods can be applied to serve this purpose. Support vector regression (SVR) is a novel and powerful data-based method for predicting time series. This study proposes the genetic algorithm (GA)–SVR hybrid algorithm that combines the GA for parameter calibration and the SVR method for the simulation and prediction of groundwater levels. The GA–SVR algorithm is applied to three observation wells in the Karaj plain aquifer, a strategic water source for municipal water supply in Iran. The GA–SVR's groundwater-level predictions were compared to those from genetic programming (GP). Results show that the randomized approach of GA–SVR prediction yields R2 values ranging between 0.88 and 0.995, and root mean square error (RMSE) values ranging between 0.13 and 0.258 m, which indicates better groundwater-level predictive skill of GA-SVR compared to GP, whose R2 and RMSE values range between 0.48–0.91 and 0.15–0.44 m, respectively.

Highlights

Groundwater is a vital source of municipal, industrial, and agricultural water use worldwide
This paper shows that the application of data-based groundwaterlevel prediction models constitutes an alternative method to predicting groundwater levels bypassing the implementation of numerical groundwater simulations when aquifer characteristics are poorly known
The aim of this division is assessing the ability of the genetic algorithm (GA)–Support vector regression (SVR) method in estimating groundwater level with non-trained data, i.e., testing the predictive accuracy of the trained SVR with a data set not used in its training

Summary

Introduction

Groundwater is a vital source of municipal, industrial, and agricultural water use worldwide. This work applied three different approaches to select the training and testing data sets of the GA–SVR method.

Results

Conclusion