Comparison of interpolation methods to improve the prediction of groundwater surface level using deep learning

Document Type : Research Paper

Authors

Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.

10.22059/jwim.2024.372424.1145

Abstract

Groundwater resources are an important factor in managing and maintaining water that is used for drinking water, irrigation and other purposes. Groundwater level forecasting is very important for assessing total water resources and their allocation, contributing to water sustainability and drought mitigation. Sometimes, due to the presence of obstacles such as unfavorable weather conditions, blocked roads, or lack of equipment and people, measurements are not carried out for months. On the other hand, accurate and abundant groundwater level data helps to predict various consequences related to groundwater management and ecosystem health. Nevertheless, completing the missing data and improving them by interpolation method helps effectively in predicting the stability level by deep learning method. . In this study, the Azarshahr aquifer, which has recently faced a significant drop in the underground water level, was examined monthly from 1397 to 1400. Also, in order to complete the data that was not measured for any reason, kriging interpolation methods and M5P algorithm were used. By analyzing each method, the M5P method with the minimum root mean square error of 1.83 meters and correlation coefficient of 0.975 was the best. It had the function. On the other hand, in order to predict the underground water level, the data was divided into 70 and 30 calibration and accuracy measurements, and the deep learning (DL) method was used, which was acceptable with an error of 1.408 meters and an accuracy of 88%. And it can be used in future research for better management of water resources.

Keywords

Main Subjects

References

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Water and Irrigation Management
Volume 14, Issue 3
December 2024
Pages 665-679

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