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Open AccessArticle

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

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African Centre of Excellence in Internet of Things, University of Rwanda, Kigali 3900, Rwanda
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Department of Information Technology, Copperbelt University, Kitwe 21692, Zambia
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Department of Computer Science, State University of Zanzibar, P.O. Box 146, Zanzibar, Tanzania
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Inmarsat, 99 City Road, London EC1Y 1AX, UK
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Department of Information Technology, SRM Institute of Science and Technology, Chennai 603203, India
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Author to whom correspondence should be addressed.
Hydrology 2020, 7(3), 59; https://doi.org/10.3390/hydrology7030059
Received: 7 May 2020 / Revised: 18 June 2020 / Accepted: 5 July 2020 / Published: 18 August 2020
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). View Full-Text
Keywords: seasonal forecasting; ensemble model; groundwater level; machine learning; artificial neural network; predictive modeling; eastern Rwanda seasonal forecasting; ensemble model; groundwater level; machine learning; artificial neural network; predictive modeling; eastern Rwanda
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Kombo, O.H.; Kumaran, S.; Sheikh, Y.H.; Bovim, A.; Jayavel, K. Long-Term Groundwater Level Prediction Model Based on Hybrid KNN-RF Technique. Hydrology 2020, 7, 59.

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