Verification of a Three Dimensional Advection Dispersion Model Using Dye Release Experiment

The study aims to investigate transport processes within the entrance of a coastal lagoon through estimating the advectiondispersion coefficients of the estuary. To this end, an extensive observational data set including water level variation and current has been used for the hydrodynamic calibration of the model. Simulation of water quality variation with time requires mathematical modelling based on the advection and dispersion phenomenon. The advection-dispersion model is setup using a MIKE3 software platform. The model is calibrated using data obtained through monitoring the dilution and movement of a tracer (Rhodamine WT), which is introduced into the water column during a number of experiments at various locations within the study area

Estuarine flood modelling using artificial neural networks

© 2014 IEEE. Prediction of water levels at estuaries poses a significant challenge for modelling of floods due to the influence of tidal effects. In this study, a two-stage forecasting system is proposed. In the first stage, the tidal portion of the available records is used to develop a tidal prediction system. The predictions of the first stage are used for flood modelling in the second. Experimental results suggest that the proposed flood modelling approach is advantageous for forecasting flood levels with more than 1 hour lead times

Application of neural network to flood forecasting an examination of model sensitivity to rainfall assumptions

This paper describes the development of a back-propagation Neural Network model for predicting flood and its application to a short response catchment. Common operational flood forecasting is based on traditional physically based and conceptual methods. These methods, despite being based on robust physical laws, have limitations. Data-driven models are plausible alternatives to physically based methods for certain flood forecasting applications. However, there is still a need for further demonstration of their ability in flood forecasting in order to build enough confidence for their application in practice. Among the aims of developing forecasting models, is utilizing them as a decision support system. To ensure applicability of the system for real-world application, limitations of the model should be outlined. Initial simulations conducted on a small-response time catchment outlined sensitivity of the accuracy of the model to rainfall and the way it is addressed. In this study, uncertainties associated with unseen portion of rainfall at the time that the actual forecasting is carried out, during real-world flood event are emulated. Four scenarios are considered for this study, rainfall is assumed known, rainfall is naively predicted, rainfall is treated as hidden variable and rainfall is predicted using axillary ANN. The study shows that a proposed ANN is adequately skilled for short-term flood predictions; however variability in rainfall within span of few hours limits reliability of predictions as time horizon increases. In addition the study proposes several directions that may improve the forecasts despite inherit limitations. In particular, subsequent to qualitative performance analysis, it was observed that optimization goal defined for ANN, Root Mean Square Error (RMSE) is not fully aligned with purpose of a decision support system and hence can be pursued as a potential research direction

The prediction of flood damage in coastal urban areas

The increase of impervious surfaces in the urban area triggers a flood. A flood occurs area with a dense population that will result in a lot of damage. The flood simulation itself is not adequate to calculate the flood damage, as it only shows the flood depth and extent. It needs the capability of mapping software to map the vulnerable area. Accordingly, the research study's aim is to propose the methodology to predict the flood damage on the coastal urban area by combining the flood simulation model with GIS mapping software. MIKE FLOOD and ArcGIS were used to represent the flood simulation model and mapping software. The flood depth and inundation area were calculated with MIKE FLOOD; meanwhile, the residential house was mapped using ArcGIS. Both of MIKE FLOOD and ArcGIS were then combined to obtain the flood depth in each residential house. Moreover, to value the flood damage in monetary terms, the depth-damage curve and average house prices were applied. The result shows that the majority of the inundation caused by riverine flood and coastal area is the place where the largest inundation area occurs. As the flood appears in a residential area, the flood damage of the residential building in terms of annual average damage (AAD) was obtained with the amount of $8,716,227.67 calculated from six AEPs (50%, 20%, 10%, 5%, 2%, and 1%).</p