As a levee failure and the consequent flooding cause significant financial losses and sometimes human casualties, they have led to considerable concern among city officials. Therefore, researchers have devoted considerable effort to investigating the hydraulic characteristics of sudden transient flow in the form of propagated waves to inundation areas during a levee and/or dam failure. A large number of studies, however, have mostly focused on simple one-dimensional cases investigated numerically and/or experimentally, and thus, important hydraulic characteristics, particularly near the failure zone, have not been adequately captured because of three-dimensional complexities. Taking these complexities into consideration, this study conducts a large-scale experiment to examine the characteristics of wave propagation in an open area caused by a gradual levee failure. From the experimental observations, this study provides the propagation speed of a wave front and suggests a formula for the maximum flood depth corresponding to the peak flood wave in the inundation area. We expect the findings to provide hydraulic engineers and scientists with fundamental insights into transient flow during a gradual levee failure. By contributing to our theoretical understanding, the measurements can also be used as validation tools for future numerical simulation and are likely to contribute to the establishment of emergency action plans that can help city officials cope with flood inundation.

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To date, physical, numerical or data-driven models have been used to forecast water surface elevation in rivers for specific times or locations in the literature. Recently, the trend of forecasting water surface elevation changed from physical and numerical models to data-driven models with the help of the development of big data processing technology and fast simulating time of data-driven models. In this study, a data-driven model with Long Short-Term Memory (LSTM) was developed using TensorFlow, one of the famous deep learning frameworks and forecasting of water surface elevation affected tidal river was performed in Hangang River, Korea. From many types of field measurements, the hourly hydrological data, precipitation, outlet discharge of dam upstream and tidal levels were selected as the input dataset through a t-test and a p-value. In particular, the hybrid activation function was proposed to alleviate the vanishing gradient and dying neuron problems generally issued in the application of the activation function. The model showed that the root mean square error (RMSE) and peak error (PE) decreased by 0.22–0.25 m and 0.11–0.21 m, respectively, and the Nash-Sutcliffe efficiency (NSE) increased up to 79.3%–97.0% compared with the single activation functions. For w 1 = 0.6 and w 2 = 0.4 in the hybrid activation function, the improvement of accuracy and the enhancement of the application range of the leading time interval were obtained through a sensitivity analysis. Moreover, the hybrid activation function showed a good performance. The forecasting results provided by this model can be used as reference data for the establishment of the emergency action plan (EAP).

Pollutants related to water quality often exist in rivers and form clusters. These pollutants adversely affect river environments and ecosystems. In Korea, the public’s interest in water quality has been increasing for decades. Many studies on water quality and pollutants in sewage treatment plants have been conducted; however, studies on the formation of flocs based on the flow characteristics of rivers are insufficient. In general, it is known that floc formation is influenced by hydraulic characteristics, such as velocity and turbulence, and that it combines them with contaminants in the river. However, studies that quantitatively analyze this topic are also insufficient. An analysis of floc formation between sediments must be conducted to understand the formation process of sediments and contaminants. Therefore, in this study, kaolin, which is a cohesive sediment, was used to quantify the floc formation process according to the mixing intensity. Turbidity was analyzed to observe the amount of floc formation, and samples were collected to confirm the concentration. Additionally, the turbidity concentration relationship according to the mixing intensity was quantified using an optical microscope. Regarding the mixing intensity, when the rotation speed was 200 rpm or more, the separation of the flocs was dominant. In contrast, when the rotation speed was 100 rpm or less, turbidity changes due to sedimentation and floc formation were dominant. Analyzing mixing intensities and their association with the flow characteristics of rivers may be useful for the management of contaminants in rivers.

We have developed the SIND (scientific interpolation for natural disasters) model to forecast natural hazard zone for storm surge. Most previous studies have been conducted to predict hazard zone with numerical simulations based on various scenarios. It is hard to predict hazard zone for all scenarios and to respond immediately because most numerical models are requested a long simulation time and complicated postprocess, especially in coastal engineering. Thus, in this study, the SIND model was developed to overcome these limitations. The principal developing methods are the scientific interpolation for risk grades and trial and error for parameters embedded in the governing equation. Even designed with hatch files, applying disaster characteristics such as the risk propagation, the governing equation for storm surge in coastal lines was induced from the mathematical solver, COMSOL Multiphysics software that solves partial differential equations for multiple physics using FEM method. The verification process was performed through comparison with the official reference, and the accuracy was calculated with a shape similarity indicating the geometric similarity of the hazard zone. It was composed of position, shape, and area criteria. The accuracy of about 80% in terms of shape similarity was archived. The strength of the model is high accuracy and fast calculation time. It took only less than few seconds to create a hazard map for each scenario. As future works, if the characteristics of other disasters would be understood well, it would be able to present risk propagation induced from each natural disaster in a short term, which should help the decision making for EAP.