Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/50919
Title: Adaptive neuro-fuzzy inference system for flood forecasting in a large river system
Authors: Nguyen, Khac Tien Phuoc
Keywords: DRNTU::Engineering::Civil engineering::Water resources
Issue Date: 2012
Source: Nguyen, K.-T. P. (2012). Adaptive neuro-fuzzy inference system for flood forecasting in a large river system. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: This research investigated the application of Adaptive Neuron-Fuzzy Inference System (ANFIS) as a data-driven model for flood forecasting in the Lower Mekong, chosen as a case study of a large river system. A systematic study of the impacts of inputs such as the water levels at the station of interest, upstream stations and main tributaries and rainfall in the sub-basin on forecast accuracy was carried out. The ANFIS models developed in the study performed well for 1- and 3-lead-day water level forecasts when compared to the benchmark adopted by Mekong River Commission and to the results provided by the Unified Run-off Basin Simulation conceptual runoff routing model. Two approaches to improve ANFIS model results were investigated. The first approach was to employ an output updating procedure, while the second approach adopted an on-line learning algorithm in training the ANFIS model. Greater attenuation of forecast errors was achieved in the falling phase, although improvements during the rising phase of flood events remains a challenge.
URI: https://hdl.handle.net/10356/50919
DOI: 10.32657/10356/50919
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Theses

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