The predicted rainfall from the ensemble meteorological modeling system is then used to drive a physically distributed hydrological model for flood responses in the Lanyang basin during the landfall of Typhoon Nanmadol (2011).
This paper introduces an integrated model for flood loss estimation in a river basin.
So-called "green infrastructure" projects - often involving rooftops being covered in soil and plants - are being used extensively in the US city of Philadelphia where officials say they provide a "model" for flood and sewage management.
Ten independent variables, namely land use/land cover, elevation, slope, topographic wetness index, surface runoff, landform, lithology, distance from the main river, soil texture and soil drainage were used into the FR model for flood vulnerability analysis.
This study applies an Adaptive Network-based Fuzzy Inference System (ANFIS) and a Real-Time Recurrent Learning Neural Netwith (RTRLNN) with an optimized reservoir release hydrograph using Mixed Integer Linear Programming (MILP) from historical typhoon events to develop a multi-phase intelligent real-time reservoir operation model for flood control.
The new regression models developed in this study were compared to a set of existing models recommended for flood frequency estimation in Spain.
A collection of calibrated hydrologic models for flash flood prone basins, within the City of Austin, is used for the comparison.
Nonetheless the lack of field data and the computational cost constraints limit the extensive use of 2D models for operational flood forecasting.
Therefore, this study tries to improve the decomposition-ensemble framework and propose an adaptive middle and long-term runoff forecast model especially for flood seasons.
There are many models as proposed by different researchers, but it is very important to evaluate the accuracy and success rate to validate the model used for flood susceptibility analysis (Chung and Fabbri 2003; Tien Bui et al. 2012).
