Context and Temporal Aware Attention Model for Flood Prediction

Abstract

To minimize damages brought by floods, researchers pay special attentions to solve the problem of flood prediction. Multiple factors, including rainfall, soil category, the structure of riverway and so on, affect the prediction of sequential flow rate values, but factors are not always informative for flood prediction. Extracting discriminative and informative features thus plays a key role in predicting flow rates. In this paper, we propose a context and temporal aware attention model for flood prediction based on a quantity of collected flood factors. We build our model on top of Long Short-Term Memory (LSTM) networks, which selectively focuses on informative factors and pays different levels of attentions to the outputs of different cells. The proposed CT-LSTM network assigns time-varying weights to input factors at all the cells of LSTM network, and allocates temporal-dependent weights to the outputs of each LSTM cell for boosting prediction performance. Experimental results on a benchmark flood dataset with several comparative methods demonstrate the effectiveness of the proposed CT-LSTM network for flood prediction.