Comparison Of A Data-driven Model And A
Physical Model For Flood Forecasting
Author(s)
J. Ji, C. Choi, M. Yu & J. Yi
Abstract
Flooding from torrential rain occurs in a short amount of time, while drought
lasts for a longer period; the former may inflict huge losses in terms of both life
and property. For these reasons, considerable research has been performed in the
field of flood control system development. A physical model is mainly used for
flood forecasting and warning. However, physical rainfall-runoff models for the
conventional flood forecasting process require extensive information and data,
and include uncertainties that can accumulate errors during the modeling
process. On the other hand, ANFIS, which is a data-driven model combining the
neural network and fuzzy techniques, can decrease the amount of physical data
required for the construction of a conventional model and easily construct and
evaluate a flood forecasting model using only rainfall and water level data.
However, data-driven models have the disadvantage that they do not provide
mathematical and physical logic, so that there are no logical correlations between
the input and output data of the model. This study analyzes the characteristics of
a data-driven model, ANFIS, according to its functional options and input data,
such as changes in the clustering radius and the training data length. In addition,
the suitability of ANFIS is evaluated through comparison with the results of
HEC-HMS, which is widely used for rainfall-runoff models. In this study, the
neuro-fuzzy technique is applied to the Cheongmicheon Basin using the
observed precipitation and stream level data from 2008 to 2011.
Keywords: ANFIS, HEC-HMS, neuro-fuzzy technique, flood forecasting.
Keywords
ANFIS, HEC-HMS, neuro-fuzzy technique, flood forecasting
Related Book
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