Spatio-temporal asynchronous co-occurrence pattern for big climate data towards long-lead flood prediction

Chung-Hsien Yu, Dong Luo, Wei Ding, Joseph Paul Cohen, David L. Small, Shafiqul Islam

Abstract: Recent research efforts aim at utilizing Big Climate Data to predict floods 5 to 15 days in advance. Improvements in the prediction of heavy precipitation, a major factor related with flood occurrences, have lagged behind due to the high-dimensionality and non-linearity in the weather, hydriology and dydraulic systems. In this paper, we introduce Spatio-Temporal Asynchronous Co-Occurrence Pattern to associate heavy precipitation with dense precipitable water and explore long-lead flood prediction from the machine learning perspective. Our model predicts one location's flooding risk by connecting the heavy precipitation with its preceding precipitable water through an association mining method. We discover asynchronous co-occurrence location and discuss a spatio-temporal ensemble learning method for predictive modeling. Our framework requires less computational cost and smaller train data compared to other existing approaches. In addition, the framework is designed to be scalable and allows distributed computing. Our real-world case study in the state of Iowa has achieved 87% accuracy on predicting the heavy precipitations which trigger severe floods at least 9 days in advance.