Development of a Regional Neural Network for Coastal Water Level Predictions; Journal article

摘要

This paper presents the development of a Regional Neural Network for Water Level (RNN-WL) predictions, with an application to the coastal inlets along the South Shore of Long Island, New York. Long-term water level data at coastal inlets are important for studying coastal hydrodynamics sediment transport. However, it is quite common that long-term water level observations may be not available, due to the high cost of field data monitoring. Fortunately, the US National Oceanographic and Atmospheric Administration (NOAA) has a national network of water level monitoring stations distributed in regional scale that has been operating for several decades. Therefore, it is valuable and cost effective for a coastal engineering study to establish the relationship between water levels at a local station and a NOAA station in the region. Due to the changes of phase and amplitude of water levels over the regional coastal line, it is often difficult to obtain good linear regression relationship between water levels from two different stations. Using neural network offers an effective approach to correlate the nonlinear input and output of water levels by recognizing the historic patterns between them. In this study, the RNN-WL model was developed to enable coastal engineers to predict long-term water levels in a coastal inlet, based on the input of data in a remote NOAA station in the region. The RNN-WL model was developed using a feed-forwards, back-propagation neural network structure with an optimized training algorithm. The RNN-WL model can be trained and verified using two independent data sets of hourly water levels. The RNN-WL model was tested in an application to Long Island South Shore. Located about 60-100 km away from the inlets there are two permanent long-term water level stations, which have been operated by NOAA since the 1940s.