In this report two Agent-Based Models, the Passenger Model and the Transfer Model, will be constructed and analyzed. The objective is to make a realistic model inspired by a cell-based model to simulate passenger flows on a train platform. We will start by studying the cell-based model and we will explain which aspects have been applied to our model. Then the Passenger Model, which can also be applicated to different environments, will be introduced. After that the Transfer Model, which is an even more realistic model for a train platform, is constructed. The models are used to analyze different types of platforms to investigate what an optimal platform would look like. These models can help visualizing passenger flows and can be used to analyze the performance of existing platforms and platforms that have yet to be designed.

The derivation of water quality indicators is of importance, especially in coastal areas, as most of the economic activities are located here. However, the availability of high-spatial-resolution water quality information in coastal zones is limited. Nowadays, high-resolution satellite data is becoming available and can fill in this knowledge gap. This satellite data contains spectral reflectances, so a model needs to be designed to map these reflectances to water quality indicators. In this thesis, a Gaussian process regression (GPR) method will be introduced and analyzed extensively in terms of covariance functions, hyperparameters and computational costs. Remote sensing data is collected from the Sentinel-2 mission and the in-situ data is obtained from the ODYSSEA programme. The Matérn 3/2 kernel produces the best results and these are compared with the current models that rely on machine learning techniques. GPR shows promising results in terms of estimation accuracy and chlorophyll-a maps are made for different areas and depths. Various approximation methods are tested to speed up the computation time. Singular value decomposition shows promising results for doing predictions to reduce the computation time. Moreover, GPR can handle limited availability of in-situ data well and uncertainty quantification is induced by the Bayesian framework.