Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting

Abstract

Abstract:The dynamic relationship between watershed characteristics and rainfall-runoff has been widely studied in recent decades. Since watershed rainfall-runoff is a non-stationary process, most deterministic flood forecasting approaches are ineffective without the assistance of adaptive algorithms. The purpose of this paper is to propose an effective flow forecasting system that integrates a rainfall forecasting model, watershed runoff model, and real-time updating algorithm. This study adopted a grey rainfall forecasting technique, based on existing hourly rainfall data. A geomorphology-based runoff model can be used for simulating impacts of the changing geo-climatic conditions on the hydrologic response of unsteady and non-linear watershed system, and flow updating algorithm were combined to estimate watershed runoff according to measured flow data. The proposed flood forecasting system was applied to three watersheds; one in the United States and two in Northern Taiwan. Four sets of rainfall-runoff simulations were performed to test the accuracy of the proposed flow forecasting technique. The results indicated that the forecast and observed hydrographs are in good agreement for all three watersheds. The proposed flow forecasting system could assist authorities in minimizing loss of life and property during flood events. OPEN ACCESSWater 2015, 7 1841

Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling

“…Ho and Lee [25], in their paper Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting, develop a real-time flood prediction system by coupling a precipitation prediction model, a geomorphology-based discharge model and an updating algorithm. Observed hourly precipitation data are employed in the grey precipitation prediction model.…”

Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling

“…Previous research indicates that adaptive algorithms are key in deterministic flood prediction models owing to the intrinsic non-stationary nature of the rainfall-runoff process. Ho and Lee [25], in their paper Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting, develop a real-time flood prediction system by coupling a precipitation prediction model, a geomorphology-based discharge model and an updating algorithm. Observed hourly precipitation data are employed in the grey precipitation prediction model.…”

Accurate Prediction of Streamflow Using Long Short-Term Memory Network: A Case Study in the Brazos River Basin in Texas

“…Data-driven models, and deep learning architectures in particular, have recently gained immerse applicability in hydrologic modeling as the underlying inter-relationships among the parameters are learnt, merely, through the data in an automated manner without any external effort exerted by human experts [2]- [4]. These models often out-perform the statistical models, as no prior model assumption for the input-output relation is required (examples of statistical methods can be found at [5]- [11]). An appropriately designed deep learning architecture would in turn enable us to extract the governing physical-based inter-relationships where the learning parameters are adjustable [12].…”