Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic

In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in mins) for the ETA prediction

Guiding Ebola Patients to Suitable Health Facilities: An SMS-based Approach

We propose to utilize mobile phone technology as a vehicle for people to report their symptoms and to receive immediate feedback about the health services readily available, and for predicting spatial disease outbreak risk. Once symptoms are extracted from the patients text message, they undergo complex classification, pattern matching and prediction to recommend the nearest suitable health service. The added benefit of this approach is that it enables health care facilities to anticipate arrival of new potential Ebola cases

Improvements of the shock arrival times at the Earth model STOA

Prediction of the shocks' arrival times (SATs) at the Earth is very important for space weather forecast. There is a well-known SAT model, STOA, which is widely used in the space weather forecast. However, the shock transit time from STOA model usually has a relative large error compared to the real measurements. In addition, STOA tends to yield too much `yes' prediction, which causes a large number of false alarms. Therefore, in this work, we work on the modification of STOA model. First, we give a new method to calculate the shock transit time by modifying the way to use the solar wind speed in STOA model. Second, we develop new criteria for deciding whether the shock will arrive at the Earth with the help of the sunspot numbers and the angle distances of the flare events. It is shown that our work can improve the SATs prediction significantly, especially the prediction of flare events without shocks arriving at the Earth.Comment: Submitted to JG