Predicting Water Table Fluctuations Using Artificial Neural Network

Predicting Water Table Fluctuations Using Artificial Neural Network

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Correctly forecasting groundwater level fluctuations can assist water resource managers 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, and soil moisture data produced by the Land Data Assimilation System 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 the trend of the time series of the ground measurement at the study site. An extension of the study to a regional scale was also performed at 12 available well sites in Piedmont Plateau, Maryland. Hydrologic soil types, LDAS soil moistures, and water table depths at these locations were used in the ANN modeling. The root mean square error of one month long water table depth forecast was 0.142m. However, the accuracy of the monthly forecast decreases with the increase of time. A further study to improve the accuracy of long-term water table fluctuation forecast is recommended.Figure 26. Time series of the water table depth and soil moisture content at well BA Ea 18 in Baltimore County, MD (r = -0.65). 67 Figure 27. Schematic diagram of a three layer ANN model using current soil moisture content, current water tableanbsp;...


Title:Predicting Water Table Fluctuations Using Artificial Neural Network
Author: Chung-Yu Wu
Publisher:ProQuest - 2008
ISBN-13:

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