The Louisiana coastal ecosystem is experiencing increasing threats from human flood control construction, sea-level rise (SLR), and subsidence. Louisiana lost about 4,833 km2 of coastal wetlands from 1932 to 2016, and concern exists whether remaining wetlands will persist while facing the highest rate of relative sea-level rise (RSLR) in the world. Restoration aimed at rehabilitating the ongoing and future disturbances is currently underway through the implementation of the Coastal Wetlands Planning Protection and Restoration Act of 1990 (CWPPRA). To effectively monitor the progress of projects in CWPPRA, the Coastwide Reference Monitoring System (CRMS) was established in 2006. To date, more than a decade of valuable coastal, environmental, and ground elevation data have been collected and archived. This dataset offers a unique opportunity to evaluate the wetland ground elevation dynamics by linking the Rod Surface Elevation Table (RSET) measurements with environmental variables like water salinity and biophysical variables like canopy coverage. This dissertation research examined the effects of the environmental and biophysical variables on wetland terrain elevation by developing innovative machine learning based models to quantify the contribution of each factor using the CRMS collected dataset. Three modern machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were assessed and cross-compared with the commonly used Multiple Linear Regression (MLR). The results showed that RF had the best performance in modeling ground elevation with Root Mean Square Error (RMSE) of 10.8 cm and coefficient of coefficient (r) = 0.74. The top four factors contributing to ground elevation are the distance from monitoring station to closest water source, water salinity, water elevation, and dominant vegetation height. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection