Leveraging Classification Models for River Forecasting

Ruizhou Ding, Diana Marculescu

Abstract: Prior work in river forecasting has focused on applying regression models to gage and discharge prediction since these are naturally continuous dynamical functions. On the other hand, with discretized data, classifiers can be adopted to solve this problem by predicting a conditional probability distribution. Predicting this distribution is important in at least two ways: (1) the variance of the distribution can indicate the confidence of the predicted expected values, and (2) the distribution can be used for computing the probability that the gage or discharge exceeds or falls below some threshold. This paper presents a concrete river forecasting framework with classifiers including probabilistic graphical models (PGMs) and artificial neural network classifiers (ANNCs). The proposed framework is applied on real data for the Guadalupe river basin (Texas) thereby enabling a detailed comparison among various manners of forecasting studied, along with a set of guidelines for their best use.