Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Yanlai Zhou,Jiabo Yin,Shenglian Guo,Fi-John Chang,Chong-Yu Xu

doi:10.3390/w12020578

Yanlai Zhou, Jiabo Yin + Show 3 more

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https://doi.org/10.3390/w12020578

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Abstract

It is fundamentally challenging to quantify the uncertainty of data-driven flood forecasting. This study introduces a general framework for probabilistic flood forecasting conditional on point forecasts. We adopt an unscented Kalman filter (UKF) post-processing technique to model the point forecasts made by a recurrent neural network and their corresponding observations. The methodology is tested by using a long-term 6-h timescale inflow series of the Three Gorges Reservoir in China. The main merits of the proposed approach lie in: first, overcoming the under-prediction phenomena in data-driven flood forecasting; second, alleviating the uncertainty encountered in data-driven flood forecasting. Two commonly used artificial neural networks, a recurrent and a static neural network, were used to make the point forecasts. Then the UKF approach driven by the point forecasts demonstrated its competency in increasing the reliability of probabilistic flood forecasts significantly, where predictive distributions encountered in multi-step-ahead flood forecasts were effectively reduced to small ranges. The results demonstrated that the UKF plus recurrent neural network approach could suitably extract the complex non-linear dependence structure between the model’s outputs and observed inflows and overcome the systematic error so that model reliability as well as forecast accuracy for future horizons could be significantly improved.

Highlights

Reliable and accurate flood forecasting is one of the most important tasks of operational hydrology, while it is very challenge due to the inordinately non-linear hydro-geological features and dynamic nature of climate conditions
We adopt a probabilistic forecasting methodology that hybridises unscented Kalman filter (UKF) and artificial neural networks (ANNs) to generate posterior distributions from observed and forecasted inflows for effectively reducing the predictive distributions occurring in data-driven flood forecasting to small ranges
The contribution of the UKF approach depends on modeling the non-linear correlation among hydrologic variables and on reducing the uncertainty arosing in flood forecasting

Summary

Introduction

Reliable and accurate flood forecasting is one of the most important tasks of operational hydrology, while it is very challenge due to the inordinately non-linear hydro-geological features and dynamic nature of climate conditions. High uncertainty encountered in the occurrence and magnitudes of future flood event stimulates the demands for probabilistic flood forecasting. The goal of probabilistic forecasting is to provide information about the uncertainty of the forecast [1]. Most hydrological forecast models produce deterministic forecasts, which provide the best point-value estimates rather than quantify the predictive uncertainty [2]. Probabilistic hydro-meteorological forecasts have been used frequently to communicate forecast uncertainty over the last few decades [4,5,6]. The transformation from a deterministic approach to a probabilistic approach is a development trend of flood forecasting around

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