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

Abstract

Flood forecasting is an important non-structural means of flood mitigation. An updating technique is a tool to update the forecasts of mathematical flood forecasting model based on data observed in real time, and is an important element in a flood forecasting model. An error prediction model based on a fuzzy rule-based method was proposed as the updating technique in this work to improve one-to fourhour-ahead flood forecasts by a model that is composed of the grey rainfall model, the grey rainfall-runoff model and the modified Muskingum flow routing model. The coefficient of efficiency with respect to a benchmark is applied to test the applicability of the proposed fuzzy rule-based method. The analysis reveals that the fuzzy rulebased method can improve flood forecasts one to four hours ahead. The proposed updating technique can mitigate the problem of the phase lag in forecast hydrographs, and especially in forecast hydrographs with longer lead times.

Review of the Kalman-type hydrological data assimilation

“…Streamflow is the most commonly used and sometimes the only available prognostic observation variable (Clark et al 2008, Randrianasolo et al 2014, Samuel et al 2014, Trudel et al 2014, Abaza et al 2014a. Great efforts have been made to improve streamflow forecasting using output assimilation/ error assimilation over the past two decades (Broersen 2007, Anctil et al 2003, Yu and Chen 2005, Sene 2008. The output/ error assimilation methods treat a streamflow forecast as a pure model output and update it by adding errors calculated with another independent procedure or model.…”

Improving Coastal Ocean Wave Height Forecasting during Typhoons by using Local Meteorological and Neighboring Wave Data in Support Vector Regression Models

“…Artificial intelligence and machine learning methods that have low computational cost and are easy to implement have been adopted in hydrologic forecasting. Among the various methods, artificial neural networks (ANNs) [14][15][16][17], support vector machines (SVMs) [18][19][20][21][22], and fuzzy inference models (FIMs) [23][24][25][26][27] are commonly and successfully used for forecasting various hydrologic variables. Regarding wave forecasting, ANNs were used by Deo and Sridhar Naidu [28], Tsai et al [29], and Mandal and Prabaharan [30] to forecast wave heights with inputs of present and previous wave heights.…”

Operational Probabilistic Forecasting of Coastal Freak Waves by Using an Artificial Neural Network

“…Various AI methods have been applied to hydrologic and ocean forecasting. In hydrologic forecasting, artificial neural networks [8][9][10][11][12], support vector machines [13][14][15][16][17][18][19][20], and fuzzy set theory [21][22][23][24][25][26][27] have been successfully used to perform rainfall and flood forecasting. In ocean forecasting, AI methods have been employed to forecast significant wave height.…”