document

Real-time Probabilistic Passenger Arrival Forecasting

Katona, Misha (author)

Through several contractions, stiff competition, and increasing passenger expectations, airports must evolve continually. One of the main avenues for this has been improving the efficiency of the security check- points, which are airports’ primary bottlenecks. Operational optimisation methods, such as resource and task scheduling are relatively...

master thesis 2024

document

Enhancing Baggage Handling Operations at Hub Airports through Sequential Forecasting and Resource Allocation: A Gradient Boosting Approach with MILP Optimisation

de Keijzer, Mitchell (author)

Baggage handling operations in airports have become substantially more challenging due to the surge in air transportation demand witnessed in the last three decades combined with an even higher pressure on the operations following the COVID-19 pandemic. The intricate nature of checked baggage requirements, impacting resource allocation and...

master thesis 2023

document

Deep learning approaches to short term traffic forecasting: Capturing the spatial temporal relation in historic traffic data

Kuiper, Thomas (author)

The amount of cars on the roads is increasing at a rapid pace, causing traffic jams to become commonplace. One way to decrease the amount of traffic congestion is by building an Intelligent Transportation System (ITS) which helps traffic flow optimally. An important tool for an ITS is short term traffic forecasting. Better forecasts will enable...

bachelor thesis 2023

document

Algal Bloom Forecasting: Classical Machine Learning versus Deep-Learning

Lubbers, Rob (author)

The aim of this paper is to find out which Machine Learning (ML) model predicts the concentration of Chlorophyll-a, in the Palmar lake in Uruguay best. Currently there are no such models to predict the growth in this lake. The algorithms which will be compared in this paper are a Linear Regression model and the U-Net model. We will compare the...

bachelor thesis 2023

document

Algal Bloom Forecasting using Remote Sensing with Spatially and Temporally Sparse Satellite Data

de Gruyl, Einar (author)

This research presents a method for forecasting algal blooms using remote sensing with spatially and temporally sparse satellite data. The method involves the use of multiple interpolation methods to interpolate the sparse input data. The approach is shown to be effective in predicting algal blooms in areas where data is sparse, and the results...

bachelor thesis 2023

document

Forecasting Mortgage Prepayments in Changing Interest Rate Regimes: A Hybrid Economic-Engineering Model with EMPC

Krabbenborg, Britt (author)

Banks such as Rabobank depend on multi-year mortgage prepayment forecasts in order to make provisions for the associated prepayment risks. The econometric models they use are fitted to historical data, and as a consequence their models are fitted to a decreasing interest rate regime. Given the current economic climate of increasing interest...

master thesis 2022

document

Evaluation and Improvement of a Stochastic Parametric Tropical Cyclone Rainfall Model - for Flood Impact Assessment

Arish Hasan, Arish (author)

BaCla is a new stochastic parametric precipitation model to estimate rainfall associated with Tropical cyclones (TCs) in a computationally efficient way. It is validated for a number of calibration cases along with the current benchmark IPET deterministic method. The results of these models are compared with the observed StageIV-based rainfall....

master thesis 2022

document

Toward more reliable cost estimations for asset replacement projects: Incorporating probabilistic models and the price uncertainty in the preliminary cost assessments for asset replacement projects

Krousoratis, Konstantinos (author)

The majority of existing infrastructure assets are ageing, and sooner or later, they will have reached the end of their service life. As a result, many asset owners are confronted with large-scale replacement projects in the coming decades. Annually, they allocate a significant number of resources, but these budgets are never adequate for...

master thesis 2022

document

Airport Market Share Forecasting in Multi Airport Regions

Vintges, Mark (author)

Urbanization is a worldwide trend that drives an immense increase in air traffic demand. The worldwide aviation network makes global business possible which generates economic growth, creates jobs and facilitates international tourism and trade. Many of the world's transport hubs like The Greater London Area have allocated this demand over...

master thesis 2022

document

Forecasting Models for Graph Processes: A Study on the Multi-Dimensional Case

van der Hoeven, Jelmer (author)

In the current Big Data era, large amounts of data are collected from complex systems, such as sensor networks and social networks. The emerging field of graph signal processing (GSP) leverages a network structure (graph) to process signals on an irregular domain. This thesis studies the forecasting of multi-dimensional graph processes, i.e.,...

master thesis 2022

document

A time-reversed model identification approach to time series forecasting

Sibeijn, Max (author)

In this thesis, I introduce a novel model identification approach to time series forecasting. For linear stationary processes, such as AR processes, the direction of time is independent of the model parameters. By combining theoretical principles of time-reversibility in time series with conventional modeling approaches such as information...

master thesis 2021

document

Forecasting of electric vehicle load profiles: Predicting the uncontrolled load stemming from a large group of electric vehicles as input for trading on energy markets

van der Leest, Max (author)

Exploiting electric vehicles for maintaining a stable electricity grid is a promising opportunity, which is recognized by both the academic and industrial world. Although the concept is relatively simple, the operating space containing many different stakeholders, rules and legislation is not. There are various practical issues that complicate...

master thesis 2021

document

Short-term Water Demand Forecasting at a District Level Using Deep Learning Techniques

Corredor Mora, Diego (author)

It is vital for adequate management, and operation of water distribution systems (WDS) to have reliable short-term water demand forecasts. Conventional time-series models present limitations when dealing with non-linear changes in water demand. Thus, it is proposed to employ deep learning algorithms to offer a more reliable forecast. Three...

master thesis 2021

document

On the effects of non-linearities in DP systems

Quast, Arnout (author)

In offshore operations a trend is forming where vessels are more often required to do multiple short operations within a small-time frame. Traditional mooring systems require execution time far beyond the operation time. Dynamic positioning systems offer great advantages for short time span operations such as crew transfer or lift operations. ...

master thesis 2021

document

Attention LSTM - "A comparison of ALSTM and LSTM performance in airline passenger forecast"

Şen, Sina (author)

The introduction of Attention Long Short Term Memory (ALSTM) produces an alternative to Long Short Term Memory (LSTM) by aiming to optimize information passing via removing the complexity of the cells in LSTM. In this work, the results and comparison of the performance of LSTM algorithms versus ALSTM architectures are assessed through the...

bachelor thesis 2021

document

Novel machine learning methods for short-term solar PV forecasting: Satellite image and PV generation based forecast framework for the German energy market

van Ouwerkerk, Gijs (author)

With the growing global drive to act up on climate change, the adoption of renewable energy sources such as solar photovoltaic (PV) is continuously increasing. This crucial shift poses many economic and environmental benefits, however the variability in solar PV generation may also threaten the stability of our power grid and energy supply. The...

master thesis 2021

document

Global shoreline forecasting with a clustering approach

Holland, Bram (author)

Coastal areas around the world have always been densely populated areas. However, sea-level-rise and an increase in single extreme events due to climate change, threaten the coastal areas and their inhabitants. Governmental organizations, coastal managers and various private parties thrive for better insights into long-term shoreline behavior...

master thesis 2021

document

The development of a two day ahead power forecasting model for an offshore windpark

Zweekhorst, Jules (author)

The growing fleet of offshore wind farms in the North Sea imposes a lot of stress on the energy grid due to its volatile power production. Accurate forecasting models that can predict the power generation of these wind parks could help grid operators to keep the energy grid stable and reduce the re-dispatch of energy. Therefore the objective of...

master thesis 2020

document

AutoML for Forecasting: Learning to tune hybrid forecasting models from previous tasks

Swart, Jeroen (author)

A time series is a series of data points indexed in time order. It can represent real world processes, such as demand for groceries, electricity usage and stock prices. Machine Learning (ML) models that accurately forecast these processes enable improved decision-making for reducing waste and increasing efficiency. Previous research has produced...

master thesis 2020

document

A combined forecasting and packing model to optimize decision-making under uncertainty in transportation logistics: an application to air cargo revenue management

Tseremoglou, I. (author)

Cargo airlines' Revenue Management (RM) departments oversee the acceptance/rejection process of incoming bookings. The overarching goal is to accept as many bookings as possible, hence maximizing profit, while avoiding overbooking and offloading of already accepted shipments, to ensure customer satisfaction. This whole process is characterized...

master thesis 2020