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

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...

Predicting the air quality by combining model simulations with machine learning

Combating air pollution has proven to be a difficult task for countries with rapidly developing economies. Poor air quality can be hazardous to people doing any outdoor activities. So being able to make accurate, short term air quality predictions can be very useful. However, making these predictions has proven to be quite difficult, since there...

Repetition counting in videos using deep learning

This work tackles the problem of repetition counting in videos using modern deep learning techniques. For this task, the intention is to build an end-to-end trainable model that could estimate the number of repetitions without having to manually intervene with the feature selection process. The models that exist currently perform well on videos...

HiPSS-LSTM: A Hierarchical Probabilistic LSTM model for motion forecasting

When driving a car, people can usually predict the intention of other road users with high confidence. They can spot small variations in a recent trajectory, take into account road infrastructure, traffic rules and other factors, which influence a future trajectory. As a result, they can interact with other drivers smoothly and safely. Even...

Application of Deep Learning for Spacecraft Fault Detection and Isolation

Spacecraft require high availability, autonomous operation, and a high degree of mission success. Spacecraft use sensors, such as star trackers and GPS, and actuators, such as reaction wheels, to reach and maintain a correct attitude and position. Failures in these components will have a significant negative impact on the success of the mission,...

Optimization of Strawberry Supply Chain from the Perspective of Producers

This thesis aims at maximizing the profit of a strawberry producer while satisfying the retailer's demand and meeting other constraints. The amount of strawberries to be delivered to the retailer signed in the contract is the main decision variable to be optimized in the problem. Furthermore, the transportation scheduling is also optimized to...

Assessing Global Applicability of a Long Short-Term Memory (LSTM) Neural Network for Rainfall-Runoff Modelling

Rainfall-runoff modelling is essential for short- and long-term decision-making in the water management sector. The accuracy of streamflow predictions of hydrologic models increases with the availability of and the access to streamflow observations. Therefore, one of the key challenges in the field of hydrology is to produce Predictions in...

Digital Soil Mapping based on PDFs of Cone Penetration Tests and Vibro Cores using Image Processing and Machine Learning

Digital Soil Mapping (DSM) of soil types in geotechnical project areas is a top priority. These maps are often used in decision making and can have significant consequences related to costs and risks. Usually, these maps are generated by digital soil models that interpolate soil types at known locations. In practice, conventional spatial...

Operational Streamflow Drought Forecasting for the Rhine River at Lobith Using the LSTM Deep Learning Approach

Under future warmer climates, drought events are projected to occur more frequently with increasing impacts in many regions and river basins. This study focuses on exploring the potential of the LSTM deep learning (DL) approach for operational streamflow drought forecasting for the Rhine River at Lobith with a lead time (LT) of up to 46 days. ...