mirage

The data-driven approach as an operational real-time flood forecasting model.

DSpace/Manakin Repository

 

Search DR-NTU


Advanced Search Subject Search

Browse

My Account

The data-driven approach as an operational real-time flood forecasting model.

Show full item record

Title: The data-driven approach as an operational real-time flood forecasting model.
Author: Nguyen, Phuoc Khac-Tien.; Chua, Lloyd Hock Chye.
Copyright year: 2011
Abstract: Accurate water level forecasts are essential for flood warning. This study adopts a data-driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People’s Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four- and five-lead-day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto- and cross-correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi-step-ahead error prediction was superior to the fully recursive procedure. The RAR-based partial recursive updating procedure significantly improved three-, four- and five-lead-day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR-based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest.
Subject: DRNTU::Engineering::Civil engineering::Water resources.
Type: Journal Article
Series/ Journal Title: Hydrological processes
School: School of Civil and Environmental Engineering
Rights: © 2011 John Wiley & Sons, Ltd.

Files in this item

Files Size Format View

There are no files associated with this item.

   

DOI Query

- Get published version (via Digital Object Identifier)
   

This item appears in the following Collection(s)

Show full item record

Statistics

Total views

All Items Views
The data-driven approach as an operational real-time flood forecasting model. 206

Total downloads

All Bitstreams Views
hyp8347.pdf 18

Top country downloads

Country Code Views
China 6
Singapore 6
United States of America 4
Japan 2

Top city downloads

city Views
Singapore 6
Beijing 3
Mountain View 3
Nanjing 1