  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.
