EXPERIENCE IN AIR POLLUTION PREDICTION BY STATISTICAL AND STOCHASTIC PREDICTION TECHNIQUES

Air pollution prediction with statistical algorithms

Air pollution prediction via multi-label classification

A Bayesian network classifier can be used to estimate the probability of an air pollutant overcoming a certain threshold. Yet multiple predictions are typically required regarding variables which are stochastically dependent, such as ozone measured in multiple stations or assessed according to by different indicators. The common practice (independent approach) is to devise an independent classi...

Applying Ensemble Learning Techniques to ANFIS for Air Pollution Index Prediction in Macau

Nowadays, the conception on environmental protection is increasingly rising up and one of the critical environmental issues is the air pollution due to the rapidly growth of economy and population. Hence, a significant forecasting for the air pollution index (API) becomes important as it can act as the alarm for alerting our awareness in the air pollution issue. In this research, an architectur...

Multivariate statistical techniques for parallel performance prediction

Performance prediction can play an important role in improving the efficiency of multicomputers in executing scalable parallel applications. An accurate model of program execution time must include detailed algorithmic and architectural characterizations. The exact values for critical model parameters such as message latency and cache miss penalty can often be dificult to determine. This resear...

Statistical and Stochastic Problems in Ocean Modeling and Prediction

My project addresses statistical and stochastic problems in the following fields: Lagrangian prediction (1), Lagrangian data assimilation (2), development and validation of Lagrangian stochastic models (LSM) (3). The long range scientific objectives of this study comprise rigorous determining limits of predictability for Lagrangian motion in semi-enclosed seas, littoral zones, and straits on ti...