![]() ![]() It is not easy to develop a model that can deal with all hydrological processes in tidal rivers. These hydrological processes are complex, have mutual interactions, and are the driving forces for other sedimentological, biological, and chemical processes. Considering all the aforementioned information, accounting for all physical processes in tidal rivers is challenging. This density difference is largely caused by differences in temperature and salinity however, salinity is by far the dominant factor affecting tidal river dynamics. Upon circulation and mixing, the 2% difference in the densities of fresh and seawater creates a pressure gradient in the horizontal direction that affects the water flow. ![]() A major climate factor affecting estuaries is wind wind creates waves, which affect water circulation and the mixing of fresh and seawater. Flooding from the upstream basin can alter the salinity profile and interrupt the tidal cycle. Longer-period effects from storms and seasonal fluctuations influence salinity. Because of the rotation of the Earth and the varying strength of the gravitational pull from the Moon and Sun, the water level varies quasiperiodically every 12.25 h or twice every lunar day. The water level in a tidal river changes because of the interaction between riverine and marine factors. Complex factors contribute to the water level in tidal rivers the water level is affected by not only the upstream river discharge but also ocean tides. The water level forecasting model accurately and reliably predicted the water level at the Taipei Bridge gauging station.Īn estuary is a transition zone with complex flow conditions in which a river enters the ocean. Water level data collected from gauging stations in the Tanshui River in Taiwan during typhoons were used to assess the feasibility of the proposed model. The proposed model is conceptually simple and highly accurate. Summing these two forecasted components enables the forecasting of the water level at a location in the tidal river. The ocean component at a location 1 h ahead can be forecast using the observed ocean components at the downstream gauging stations, and the corresponding stream component can be forecast using the water stages at the upstream gauging stations. The forecasting model is obtained through stepwise regression on these components. These IMFs are then used to reconstruct the ocean and stream components that represent the tide and river flow, respectively. EEMD is used to decompose water level signals from a tidal river into several intrinsic mode functions (IMFs). Unlike more complex hydrological models, the main advantage of the proposed model is that the only required data are water level data. In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. ![]()
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