The data-driven approach as an operational real-time flood forecasting model
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...
Saved in:
Main Authors: | Nguyen, Phuoc Khac-Tien, Chua, Lloyd Hock Chye |
---|---|
Other Authors: | School of Civil and Environmental Engineering |
Format: | Article |
Language: | English |
Published: |
2012
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/98067 http://hdl.handle.net/10220/8865 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Adaptive neuro-fuzzy inference system for flood forecasting in a large river system
by: Nguyen, Khac Tien Phuoc
Published: (2012) -
Comparison between response surface models and artificial neural networks in hydrologic forecasting
by: Yu, Jianjun, et al.
Published: (2014) -
Development of novel systems-analysis methodologies for supporting flood forecasting and uncertainty assessment
by: Yu, Jianjun
Published: (2014) -
Entrainment and mixing layer oscillations induced by a flow beneath a rectangular compartment
by: Chua, Lloyd Hock Chye.
Published: (2009) -
Mixing between sea and fresh water layers in a floating storage tank with a concentric bottom opening
by: Chua, Lloyd Hock Chye.
Published: (2009)