To keep the Panama Canal operational year round, the water level in the canal is maintained above the minimum depth. As water is passed through the locks and into the oceans on either side, the canals main reservoir looses fresh water, and this water is then replaced by the canals secondary reservoir which is Lake Alajuela. The Lake Alajuela reservoir is supplied of water by two large rivers, bringing in a sediment flux and decreasing the reservoirs capacity over time. Sedimentation sets a time limit to the use of the reservoir, and understanding this process and predicting the future morphological changes to occur in the reservoir are key to ensure continuation of operations in the Panama Canal. To predict the quantities of local sedimentation in the reservoir, a machine learning model is trained with features extracted from bathymetric models covering various years. These features are computed values that correlate with morphological processes. As such, the adaptiveness of a machine learning model along with the ability to extract morphological features from any elevation model provides the possibility to predict future sedimentation where other methods with empirical and numerical methods do not suffice due to a lack of data and restrictive parameters. Three types of machine learning algorithms and 21 features were initially tested for the purpose of the morphological modelling, during which the SVR model was the most successful. The testing was done on the R´ıo Chagres basin as well as the R´ıo Pequen´ı basin, both located in Lake Alajuela. Extensive hyperparameter tests were done to optimize and further test the performance of the SVR. Depending on the study area and the scale of the morphological behavior occurring, slightly different sets of features were most effective. Nevertheless, in both study areas tested the runoff model has proven to be a key factor for predicting the sedimentation and achieved a 70 to 80% accuracy for predicting zones of low or high sedimentation. The same model trained with the data from 1997 to 2018 was then used to predict 6 years into the future where more dynamic morphological behavior will occur according to the model. A downstream moving sediment front in the R´ıo Chagres basin is recognized and predicted to have moved 500 meters between the years 2018 and 2024. Such a sediment front travelling too far into the reservoir could have catastrophic consequences for all operations of the Panama Canal, making the awareness and anticipation of its progression highly important.

Storing accurate models of complex geometries in a compact way has become an increasingly challenging issue, especially when dealing with large datasets. One of such datasets is Cobra-Groeninzicht's database of all trees in the Netherlands. In the gaming industry, a new technique is being used to generate tree models: the L-system. An L-system stores a string representation of the structural model of a tree, with the added possibility for recursive modelling using growing rules. This format proves a promising alternative to more traditional methods of storing complex geometries. However, it remains unclear whether it can be an accurate enough representation for modelling and analysing real-life trees.

In this research project, the AdTree algorithm is used to reconstruct a skeleton from a point cloud of a single tree. This skeleton is then transformed to an L-System string format, as well as a CityJSON format (both in JSON structure). The L-system format comes with the advantage that it allows for several methods of increasing its compactness further (growing, generalisation). The overall size of these files also indicates fewer storage space is needed to store the tree geometry. The quality of the L-System skeleton is nearly equal to the input, the skeleton generated by. Assuming it can be read and drawn using a Turtle program, the L-system thus allows for storing the same geometric information more compactly than traditional storage formats, with sufficient accuracy, and the added possibilities of growing or generalising the model.