The strategy of building a flood forecast model by neuro-fuzzy network

Autor:	Li-Chiu Chang, Shen-Hsien Chen, Fi-John Chang, Yong-Huang Lin
Rok vydání:	2006
Předmět:	Hydrology Adaptive neuro fuzzy inference system Artificial neural network Neuro-fuzzy Computer science Inference system Feed forward Fuzzy control system computer.software_genre Fuzzy logic Flood forecast Data mining computer Water Science and Technology
Zdroj:	Hydrological Processes. 20:1525-1540
ISSN:	1099-1085 0885-6087
Popis:	A methodology is proposed for constructing a flood forecast model using the adaptive neuro-fuzzy inference system (ANFIS). This is based on a self-organizing rule-base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall-runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self-constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back-propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model. Copyright © 2005 John Wiley & Sons, Ltd.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::53f7173759b1fbf696926461bc76da59 https://doi.org/10.1002/hyp.5942 Zobrazit plný text záznamu Plný text