KNN, An Underestimated Model for Regional Rainfall Forecasting
This work addresses rainfall forecasting for hydrology and meteorology applications, but it is incremental as it applies existing methods to a specific regional dataset.
The paper tackled regional rainfall forecasting by comparing multiple machine learning models, including deep learning methods, and found that K-Nearest Neighbor (KNN) outperformed others in handling precipitation data uncertainty, with specific performance metrics implied but not detailed in the abstract.
Regional rainfall forecasting is an important issue in hydrology and meteorology. This paper aims to design an integrated tool by applying various machine learning algorithms, especially the state-of-the-art deep learning algorithms including Deep Neural Network, Wide Neural Network, Deep and Wide Neural Network, Reservoir Computing, Long Short Term Memory, Support Vector Machine, K-Nearest Neighbor for forecasting regional precipitations over different catchments in Upstate New York. Through the experimental results and the comparison among machine learning models including classification and regression, we find that KNN is an outstanding model over other models to handle the uncertainty in the precipitation data. The data normalization methods such as ZScore and MinMax are also evaluated and discussed.