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Bioengineering Seminar Series: John Chung-Yu Wu
Friday, September 26, 2008
11:00 a.m.
Room 2108, Chemical and Nuclear Engineering Bldg.
For More Information:
Professor Adel Shirmohammadi
(301) 405-1185

Predicting Water Table Fluctuations Using Artificial Neural Networks

Presented by John Chung-Yu Wu
Graduate Student, Biological Resources Engineering, University of Maryland

Correctly forecasting groundwater level fluctuations can assist water resource mangers and engineers in efficient allocation of the regional water needs. Modeling such systems based on satellite remotely sensed data may be a viable option to predict water table fluctuations. Two types of water table prediction models based on Artificial Neural Network (ANN) technology were developed to simulate the water table fluctuations at two well sites in Maryland. One was based on the relationship between the variations of brightness temperature and water table depth. The other one was based on the relationship between the changes of soil moisture and water table depth. Water table depths recorded at these two wells, brightness temperature retrieved from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E), and soil moisture data produced by the Land Data Assimilation System (LDAS) were used to train and validate the models. Three models were constructed and they all performed well in predicting water table fluctuations. The root mean square errors of the water table depth forecasts for 12 months were between 0.043m and 0.047m for these three models. The results of sensitivity test showed that the models were more sensitive to the uncertainty in water table depth than to that in brightness temperature or in soil moisture content. This suggests that for situations where high resolution remotely sensed data is not available, an ANN water table prediction model still can be built if the trend of the time series of the data such as brightness temperature or soil moisture over the study site correlates well with that at the study site.

This Event is For: Graduate • Faculty • Post-Docs

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