Evaluating the ability of random forest and M5P tree-based models in groundwater level estimating of Saravan plain using meteorological data

Articles in Press

Document Type : Original Article

Authors

1 Department of civil engineering, Shahid Nikbakht Engineering Faculty,, University of Sistan and Baluchestan, Zahedan, Iran

2 University of Sistan and Baluchestan

3 University of Sistan and Baluchestan , Zahedan, Iran

Abstract

Groundwater is one of the key sources of water on the surface of the earth, which is important in agriculture, industry and drinking, and is considered as the guarantor of life for the future generation. In this research, 227 data series related to meteorological and groundwater parameters of Saravan plain in Sistan and Baluchistan province were used to predict and estimate the groundwater level using intelligent machine learning models. Out of this number of data series, 159 series were allocated for use in model training and 68 series for model testing in designed scenarios. The performance of random forest and M5P models was evaluated in estimating the monthly mean groundwater level of Saravan Plain. The performance of the models was compared using statistical indices of correlation coefficient (R), root mean square error (RMSE) and mean bias error (MBE). The obtained results showed that both used models have a good ability to groundwater level modeling, but the M5P model with a higher correlation coefficient has fewer errors than the random forest model. The fourth scenario of the M5P model is selected as the best model among the scenarios of two models with correlation coefficient, MBE and RMSE values of 0.961, -0.0001 and 0.727 in the training section and 0.871, zero and 0.265 in the test section, respectively. It is suggested that the groundwater level of Saravan Plain be evaluated with other methods of artificial intelligence and machine learning, and compared with the present study.

Keywords

Iranian Journal of Irrigation & Drainage

Articles in Press, Accepted Manuscript
Available Online from 29 December 2023

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