Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning

Abstract

Due to the characteristics of strong suddenness, high harmfulness, and frequent occurrence of mountain flood disasters in small watersheds, the accuracy and reliability of mountain flood forecasting are insufficient in small watersheds. This paper studies key theories and technologies, that is the uncertainty forecasting model based on hydrologic physical mechanism. We design the Bayesian Deep Learning (DL) forecasting models, it is suitable for the transfer of spatiotemporal factors caused by mountain floods to disaster probability. The models include Bayesian Linear and Bayesian Long Short-Term Memory (LSTM) model, we hope to achieve an acceptable balance between reliability (uncertainty confidence coverage) and accuracy (confidence interval width). Meanwhile, we extract effective information from multi-source and multi-dimensional hazard factors' big data. The experiment shows the differences between Bayesian DL models, the models have long-term probability forecasting ability at both, but Bayesian LSTM is superior to Bayesian Linear in terms of reliability, accuracy and computational consumption.