A Hybrid Machine Learning and Kriging Approach for Rainfall Interpolation

The machine learning technique is a computational algorithm that is currently developing and is widely used in various models because it has the advantage of predicting without limited assumptions. Therefore, the purpose of this research is to develop a machine learning method on the kriging interpolation method to overcome the unmet assumptions of stationary and normally distributed rainfall data in East Java. This research is expected to make an important contribution as input for flood hydrograph models, which will improve understanding of flood behavior and support flood early warning systems in East Java.