NEURAL NETWORK FORECASTING OF STORM SURGES ALONG THE GULF OF MEXICO

摘要

Accurate water level forecasts are of vital importance along the Gulf of Mexico as its waterways play a critical economic role for a number of industries including shipping, oil and gas, tourism, and fisheries. While astronomical forcing (tides) is well tabulated, water level changes along the Gulf Coast are frequently dominated by meteorological factors. Their impact is often larger than the tidal range itself and unaccounted for in present forecasts. We have taken advantage of the increasing availability of real time data for the Texas Gulf Coast and have developed neural network models to forecast future water levels. The selected inputs to the model include water levels, wind stress, barometric pressures as well as tidal forecasts and wind forecasts. A very simple neural network structure is found to be optimal for this problem. The performance of the model is computed for forecasting times between 1 and 48 hours and compared with the tide tables. The model is alternatively trained and tested using three-month data sets from the 1997, 1998 and 1999 records of the Pleasure Pier Station located on Galveston Island near Houston, Texas. Models including wind forecasts outperform other models and are considerably more accurate than the tide tables for the forecasting time range tested, demonstrating the viability of neural network based models for the forecasting of water levels along the Gulf Coast.