Comparison of four updating models for real-time river flow forecasting

Abstract

Four different error-forecast updating models are investigated in terms of their capability of providing real-time river flow forecast accuracy superior to that of rainfall-runoff models applied in the simulation (nonupdating) mode. The first and most widely used is the single autoregressive (AR) model, the second being an elaboration of that model, namely the autoregressive-threshold (AR-TS) updating model. A fuzzy autoregressive-threshold (FU-AR-TS) updating model is proposed as the third form of model, the fourth and final error-forecast updating model applied being the artificial neural network (ANN) model. In the application of these four updating models, the lumped soil moisture accounting and routing (SMAR) conceptual model has been selected to simulate the observed discharge series on 11 selected test catchments. As expected, it is found that all of these four updating models are very successful in improving the flow forecast accuracy, when operating in real-time forecasting mode. A less expected, but nonetheless welcome, result is that the three updating models having the most parameters, i.e. AR-TS, FU-AR-TS, and ANN, do not show any considerable advantages in improving the real-time flow forecast efficiency over that of the simple standard AR model. Thus it is recommended that, in the context of real-time river flow forecasting based on error-forecast updating, modellers should continue to use the AR model.

Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy

“…Their research put forward a new method of determining weight, which improves the stability of the prediction results. However, the probability density function [24] is not suitable for the prediction of radial flow. The wind power load forecasting method based on normal distribution is proposed by Chen et al [25].…”

“…The application of entropy theory in hydrology mainly include the derivation of the distributions and estimation of the corresponding parameters for hydrometeorological variables [26][27][28], dependence analysis [29] and runoff forecasting [30][31][32][33][34]. The cross entropy is introduced into the combination forecasting by Li et al [24,25]. Their research put forward a new method of determining weight, which improves the stability of the prediction results.…”

“…Furthermore, there is no detailed analysis of the time characteristics of historical data. At present, some scholars are paying attention to combined forecasting in the field of rainfall forecasting [23][24][25]. In Cui's study [23], the wavelet analysis method aims to determine the weight reconstruction of the rainfall forecast, yet the time distribution of historical data is not considered.…”

“…In Cui's study [23], the wavelet analysis method aims to determine the weight reconstruction of the rainfall forecast, yet the time distribution of historical data is not considered. In Xiong's study [24] and Lu's study [25], real-time river flow or flood forecasting methods have been studied, but they are not suitable for medium and long-term prediction.…”

Updating Real-Time Flood Forecasting Using a Fuzzy Rule-Based Model/Mise à Jour de Prévision de Crue en Temps Réel Grâce à un Modèle à Base de Règles Floues

“…Many error prediction models are based on the autocorrelation of error variable (e.g. Reed, 1984;Yu, 1989;Ahsan & O'Connor, 1994;Yu et al, 2001;Shamseldin & O'Connor, 2001;Xiong & O'Connor, 2002), and some others are based on neural networks (Shamseldin & O'Connor, 2001;Xiong & O'Connor, 2002;Vojinovic et al, 2002) and probabilitybased approach (Mukherjee & Mansour, 1996). Some other updating methods that can perform probabilistic forecasts were also proposed.…”