Flood Prediction Using Rainfall-Flow Pattern in Data-Sparse Watersheds

Abstract

Real-time flood forecasting of small-and medium-sized rivers in areas with scarce hydrological data is an urgent problem that needs to be solved. Traditional hydrological model parameters cannot be fully trained owing to a lack of data; thus, results obtained by such models are not satisfactory. We need a new way to solve the forecasting problem for small-and medium-sized rivers. We found that the time series of some feature variables have evident change trajectories in spatial dimension, and the change of some feature variables in the spatial dimension has a decisive influence on flooding processes, such as the spatial distribution of rainfall. To reflect the change of feature variables in spatial dimension with to solve the problem of the lack of hydrological data, we constructed a rainfall-flow pattern composed of a spatial-temporal dynamic time warping algorithm and multi-feature algorithm to measure the similarity of hydrological time series. In the experimental watersheds, we used rainfall-flow patterns to forecast the shortterm flood streamflow, and satisfactory results were obtained. This suggests that the algorithm is suitable for hydrological studies and improves the accuracy of real-time flood forecasting for longer forecast periods. INDEX TERMS Spatiotemporal sequence data, rainfall-flow pattern matching, similarity measurement algorithm, multi-feature algorithm, ST-DTW algorithm.

A Survey on Real-Time Flood Monitoring and Alert System Using Iot

“…The paper proposed by Yuelong Zhu et. al [1] addresses the pressing challenge of real-time flood forecasting for small-and medium-sized rivers with limited hydrological data. Proposing a novel approach, the researchers utilize a rainfall-flow pattern incorporating spatial-temporal dynamic time warping and a multi-feature algorithm.…”

“…The paper proposed by Yuelong Zhu et. al [1] addresses a rainfall-flow pattern method, integrating spatialtemporal dynamic time warping and a multi-feature algorithm, to improve real-time flood forecasting accuracy for small-and medium-sized rivers with limited hydrological data.…”

Multi-Spatial Resolution Rainfall-Runoff Modelling—A Case Study of Sabari River Basin, India

“…Note that the performance is also influenced by several factors, including the type of model and the spatial and temporal resolution of input [10,29]. In this context, modeling becomes more challenging for a real-time forecast system, and both input-and hydrologic uncertainties need to be studied in association with the above mentioned factors including spatial resolution [30][31][32][33][34]. Input uncertainty dominated by rainfall plays a key role in runoff estimation, and significant research has been done on various rainfall aspects including rainfall data resolution [35][36][37].…”