Elevating Water Flow Level Predictions through Strategic Feature Elimination

Knowing the level of water at a dam represents a measure of safety and environmental health. In this research, the monitoring of the water flow level is considered the point of interest. Efficient water level monitoring is important to ensure proper decision making and long-term sustainability. Making use of the existing monitoring devices to forecast future data can help decision making in case of some unforeseen circumstances. There are a multitude of factors that can influence the level of water. In this paper, an analytic approach is reached by the implementation of gradient boosting machine learning algorithms, to forecast water levels. We propose an approach for forecasting water levels by integrating recursive feature elimination. This method aims to enhance the accuracy of the results, by ensuring that the relevant features are considered.