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...

Effects of weather data on traffic flow predictions using an LSTM deep learning model

Accurate traffic forecasts are a key element in improving the traffic flow of urban cities. An efficient approach to this problem is to use a deep learning Long Short Term Memory (LSTM) model. Including weather data in the model can improve prediction accuracy because traffic volumes are sensitive to weather changes. The aim of this study is to...

Predicting streamflow with LSTM networks using global datasets

Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually...

Short-term Earthquake Prediction with Deep Neural Networks: Finding the optimal time prior to earthquake strikes to use in predictions

Earthquakes can have tremendous effects. They can result in casualties, massive damage, and hurt the economy. Therefore, one would like to predict earthquakes as early as possible and with the highest accuracy possible. This paper contains the proposal for the optimal prediction-time, which is the time between the execution of a prediction and...

How long before strike can we predict earthquakes with an LSTM neural network?

Different methods have been studied to predict earthquakes, but the results are still far from optimal. Due to their seemingly dynamic and unpredictable nature, it has been very hard to find data correlating with earthquakes happening. But recently, various research has been done using neural networks, and some has suggested that it could...

A hybrid approach to implement the Digital Twin concept into a damage evolution prediction for composite structures

Improvement in the SHM of composite materials requires an enhanced understanding of the damage accumulation processes and helps in the way towards lighter, more optimized, and more sustainable aerospace structures. The Digital Twin concept has the potential to address this problem and may revolutionize the designing, certifying, maintaining, and...

Predicting Near-Future Demand of Self-Storage Rooms

The value of data has increased enormously over the last couple of years. Many datasets contain valuable information that can, for example, be used to make forecasts. In this thesis, the dataset of a company in the self-storage industry is analyzed. The company offers customers rental storage facilities, such as lockers, rooms, containers at...

Improving the performance of Recurrent Neural Networks for time series prediction by combining Long Short-Term Memory and Attention Long Short-Term Memory

Recurrent neural networks (RNNs) used in time series prediction are still not perfect in their predictions and improvements can still be made in the area. Most recently transformers have led to great improvements in the field of RNNs, however transformers can not be used on time series data, because the architecture of transformers does not...

Attention LSTM : Application in Stock Price Prediction on Single Companies

According to the development of data-related techniques, aimed at exploring the largest value of data, price prediction has been seen as more vital for quantifying and pricing stock. To solve this problem, the learning based algorithm became popular during modern computing techniques development. LSTM (Long Short-Term Memory) techniques attract...

Adaptations for CNN-LSTM Network for Remaining Useful Life Prediction: Adaptable Time Window and Sub-Network Training

Estimating the RUL (Remaining Useful Life) of machinery is a useful tool for maintenance and performance operations. This results in lower costs, improved safety and operational improvements.<br/>This paper proposes two adaptations to the CNN-LSTM network provided by Li et al. \cite{Li2019APrediction}, as well as exploring reproducibility,...

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...

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...

A Machine Learning Approach to Evaluating Aircraft Deviations from Planned Routes

With the increasing trend in air traffic demand and evidence of large deviations from filed flight plans, airspace capacity is not being optimally utilized. In order to improve air traffic flow and capacity management systems, so that air traffic control operators can handle more aircraft safely, air traffic predictability needs to be improved....

Deep Learning Architectures for PM2.5 and Visibility Predictions

Facing the severe air pollution phenomenon in urban areas and the subsequent low visibility event in airports, it is urgent to conduct air quality and visibility predictions to better reflect their changing trends. However, the variations of PM2.5 and visibility involve complicated physical and chemical processes, which make their accurate...

PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM

This thesis project developed an alternative PM2.5 concentration prediction model and early warning system of extreme air pollution based on the long short-term memory (LSTM) and achieved satisfying performance. To research more deeply, we divided the task into two parts. The first task was predicting the PM2.5 concentration of next 24 hours and...