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dc.contributor.advisorShirmohammadi, Adelen_US
dc.contributor.authorWu, Chung-Yuen_US
dc.date.accessioned2009-01-24T07:08:19Z
dc.date.available2009-01-24T07:08:19Z
dc.date.issued2008-11-17en_US
dc.identifier.urihttp://hdl.handle.net/1903/8826
dc.description.abstractCorrectly 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.en_US
dc.format.extent1261438 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titlePredicting Water Table Fluctuations Using Artificial Neural Networken_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentFischell Department of Bioengineeringen_US
dc.subject.pqcontrolledEngineering, Agriculturalen_US
dc.subject.pquncontrolledArtificial Neural Networken_US
dc.subject.pquncontrolledWater Tableen_US
dc.subject.pquncontrolledGroundwateren_US
dc.subject.pquncontrolledBrightness Temperatureen_US
dc.subject.pquncontrolledSoil Moistureen_US


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