Patrick Michaud

Neural Network Forecasting of Storm Surges along the Gulf of Mexico

Accurate water level forecasts are of vital importance along the Gulf of Mexico as its waterways play a critical economie 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 l 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.

Developing a goodness criteria for tide predictions based on fuzzy preference ranking

The paper deals with the developing of the tool to measure quality of predictions of water levels in estuaries and shallow waters of the Gulf of Mexico, when tide charts cannot provide reliable predictions. In future this goodness criteria of predictions will be applied to different regions.

PERFORMANCE AND COMPARISON OF WATER LEVEL FORECASTING MODELS FOR THE TEXAS PORTS AND WATERWAYS

The ports and waterways of the Texas Gulf Coast are of vital importance to the shipping industry as well as the overall US economy. Safe navigation, particularly underkeel clearance, within these shallow, confined waterways requires accurate water level forecasts. While tide tables are tabulated for a number of locations along the Texas Gulf coast, they do not meet National Ocean Service (NOS) standards due to meteorological forcing. This paper presents and compares alternative models to improve real-time water level forecasts, including a new model based on Artificial Neural Networks (ANN). All models include real-time measurements collected by the Texas Coastal Ocean Observation Network (TCOON) and the forecasts are published on the World Wide Web. The new ANN model is shown to improve considerably upon the tide tables and the other models tested and to meet NOS criteria for many locations for up to 48-hour forecasts. Model performances are compared for Corpus Christi Bay and Galveston Bay and present model limitations and future improvements are discussed.

Local and Remote Forcing of Subtidal Water Level and Setup Fluctuations in Coastal and Estuarine Environments

The relative importance of remote and local forcing on the subtidal response in Galveston Bay was studied using water level and wind data observed during the winter and spring months from 1997 to 2000. The study confirmed the importance of remote forcing through Eckman transport for the water level response and local forcing for the surface slope response. These two forcing mechanisms act independently since the estuary axis was oriented roughly orthogonal to the coastline. A neural network model was introduced which used the meteorological data to predict the water level anomaly which, when added to the harmonic tides, provided good estimates of the total water level at the bay entrance (remote forcing). 1. I N T R O D U C T I O N The need for reliable water level forecasting is increasing with the tread toward deepdraft vessels, particularly for shallow water ports along the Gulf of Mexico (NOAA, 1999). Nine of the twelve largest U.S. ports are located along the Gulf of Mexico, and ports served by the Mobile Bay Entrance and Galveston Bay Entrance account for 46% of the total U.S. tonnage (NOAA, 1999). Although the astronomical tides in the Northern Gulf of Mexico are easily predicted by conventional harmonic analysis, it is difficult to accurately predict the total water level fluctuations because of frequent meteorological events~ such as the passage of strong cold fronts. Our ilmbility to accurately predict water level anomalies (difference between the observed water level and the tide prediction) can have severe consequences. In Galveston Bay there were over 1,240 ship groundings between 1986 and 1991, with a significant number of incidents involving petrochemicals. 1Die. of Coastal and Ocean Engrg., Dept. of Civil Engrg., Texas A&M Univ., College Station, TX 77843-3136 USA; dtc~tamu.edu :Die. of Nearshore Research, Conrad Blucher Institute for Surveying and Science, Texas A&M Univ.-Corpus Christi, Corpus Christi, TX 78412 USA; PTissot~envcc00.cbi.tamucc.edu

Wireless data acquisition and logging in shallow water environments

Collecting environmental data in shallow water areas can be a challenging task. Difficulties include covering large territories and the presence of inaccessible areas due to a variety of reasons such as a soft bottom or contamination. There is also a high chance of disturbing the test area while placing the sensors. This paper describes the design and development of a remotely-operated system that transmits data wirelessly to a computer on shore in real-time. The paper also presents and discusses test results and addresses future plans.