Long-Term Groundwater Level Prediction Model Based on Hybrid KNN-RF Technique

Abstract

Reliable seasonal prediction of groundwater levels is not always possible when the quality and the amount of available on-site groundwater data are limited. In the present work, a hybrid K-Nearest Neighbor-Random Forest (KNN-RF) is used for the prediction of variations in groundwater levels (L) of an aquifer with the groundwater relatively close to the surface (<10 m) is proposed. First, the time-series smoothing methods are applied to improve the quality of groundwater data. Then, the ensemble K-Nearest Neighbor-Random Forest (KNN-RF) model is treated using hydro-climatic data for the prediction of variations in the levels of the groundwater tables up to three months ahead. Climatic and groundwater data collected from eastern Rwanda were used for validation of the model on a rolling window basis. Potential predictors were: the observed daily mean temperature (T), precipitation (P), and daily maximum solar radiation (S). Previous day’s precipitation P (t − 1), solar radiation S (t), temperature T (t), and groundwater level L (t) showed the highest variation in the fluctuations of the groundwater tables. The KNN-RF model presents its results in an intelligible manner. Experimental results have confirmed the high performance of the proposed model in terms of root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe (NSE), and coefficient of determination (R2).

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“…In the last few years, other ensemble and conventional ML models are also developed to predict GWL prediction for sustainable water resource management [39], [40]. In [39] the authors presented an ensemble model based on KNN and RF for three months ahead of groundwater table prediction based on seasonal changes.…”

“…In the last few years, other ensemble and conventional ML models are also developed to predict GWL prediction for sustainable water resource management [39], [40]. In [39] the authors presented an ensemble model based on KNN and RF for three months ahead of groundwater table prediction based on seasonal changes. In [40], the authors proposed an enhanced RF prediction model based on the combination of random features to forecast GWL using two features; temperature (Celsius) and precipitation (Millimeters).…”

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