Enhancing Prediction Accuracy and Data Handling for Environmental Applications in Innovative Modeling of Groundwater Level Fluctuations Based on the Tree Ensembles Technique

Abstract

This study developed a model to evaluate and predict fluctuations in groundwater levels by analyzing key factors influencing water reserves. Feature calculations were performed to enhance forecast accuracy, emphasizing the automatic handling of missing and noisy data before training. Using the tree ensembles learning method, the model demonstrated high accuracy in predicting water level trends in storage areas like aquifers and lakes. It showed flexibility in processing diverse input variables, including erroneous and incomplete data, without requiring complex preprocessing. This adaptability highlights the potential for real-world applications where data complexity is common. In conclusion, the study presents an effective approach for predicting groundwater level fluctuations and offers promising prospects for advancing environmental evaluation and prediction models.