Assessing the applicability of Transformer-based architectures as rainfall-runoff models

Modeling the relationship between rainfall and runoff is a longstanding challenge in hydrology and is crucial for informed water management decisions. Recently, Deep Learning models, particularly Long short-term memory (LSTM), have shown promising results in simulating this relationship. The Transformer, a newly proposed deep learning...

Multi-Horizon Prediction of Power Consumption during Dynamic Positioning Operations: Physical, Data-Driven and Hybrid Model Approaches

A significant decrease in greenhouse gas emissions can be achieved by including a prediction of future power consumption in the control of ships’ power plants during transits. Moreover, including a prediction of power consumption in the Energy Management System (EMS) during Dynamic Positioning (DP) operations can also contribute to a reduction...

Machine learning for cardiovascular modelling: Predicting time-dependent, aortic blood flow

The increase in complexity of mathematical models in an attempt to approximate reality and desire to have near real-time results have emphasized the need for fast numerical simulations. Especially in areas where classic numerical methods struggle to produce valid solutions in reasonable computational time due to their<br/>complex behaviour on...

Domain-Knowledge-Driven Explainable Product Quality Prediction: Using prior knowledge to improve explanations of quality prediction models

Explainable artificial intelligence has in recent years allowed us to investigate how many machine learning methods are creating its predictions. This is especially useful in scenarios where the goal is not to predict a variable, but to explain what influences that variable. However, the methods that have been created thus far do not focus on...

Forecasting mixed frequencies time series with missingness and its application to the domain of stock price prediction

Handling missing values is crucial for accurately forecasting time series with different sampling rates. In stock price prediction, for example, the daily stock prices and quarterly valuation figures are sampled at a different rate, and both are useful in estimating the daily stock price’s future. This research proposes combining imputation...

Traditional vs. Machine-learning methods for forecasting sandy shoreline evolution using historic satellite-derived shorelines

Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines....

To order or not to order: Predicting customer grocery shopping behaviour using multi-label classification techniques

Research and Objective: In the recent years the online grocery sector experienced an enormous uplift and evolved to a highly competitive business sector. Within this demanding environment, the need for strategic information has become extremely important, as it greatly enhances decision-making processes and the optimisation of the supply chain....

Thermosphere Modelling using Machine Learning: Artificial Neural Networks to forecast thermospheric densities and generalise beyond the properties of an acceleration data set using the Swarm satellites as study case

The Low Earth Orbit (LEO) region has been attractive to many space agencies and organisations because of its ease of access and the ideal opportunity for remote sensing. Due to the low altitudes, a satellite's orbital state is highly affected by the atmospheric drag force acting on the satellite's body. The largest variation in this drag force...

Sequential Monte Carlo method for training Neural Networks on non-stationary time series

In this thesis, we study the sequential Monte Carlo method for training neural networks in the context of time series forecasting. Sequential Monte Carlo can be particularly useful in problems in which the data is sequential, noisy and non-stationary. We compare this algorithm against a gradient-based method known as stochastic gradient descent ...

Long-Term Electricity Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term electricity load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment on the construction of excess power facilities, while an underestimate of the future load will result in...

A time series forecasting based on cloud model similarity measurement

In this paper, a local cloud model similarity measurement (CMSM) is proposed as a novel method to measure the similarity of time series. Time series similarity measurement is an indispensable part for improving the efficiency and accuracy of prediction. The randomness and uncertainty of series data are critical problems in the processing of...