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 (R2).

Keywords

• seasonal forecasting
• ensemble model
• groundwater level
• machine learning
• artificial neural network
• predictive modeling