greece has experienced particularly severe and frequent forest fires during summer 2007 together with exceptionally dry meteorological conditions culminated in distinct heat waves. the present study analyzes the time pattern of daily values of two fire risk indices (nesterov and angstrom) based on meteorological data made available for a number of gauging stations between 1 june and 31 august o...

The response of the Max-Planck Institute's ECHAM3 atmospheric general circulation model (GCM) to a prescribed decade-long positive anomaly in sea-surface temperatures (SSTs) over the North Atlantic is investigated. Two 10-year realizations of the anomaly experiment are compared against a 100-year control run of the model with seasonally varying climatological SST, using a model spatial resoluti...

Predicting rainfall remains a challenging job in weather forecasting until now. The primary focus of is the prediction at specific future period. objective this research to predict anomaly multivariate dataset. To condition, variation conditions past years must be utilized. proposed model uses supervised detection techniques for system and then compares results from each technique

Urban city environments face the challenge of disturbances, which can create inconveniences for its citizens. These require timely detection and resolution, and more importantly timely preparedness on the part of city officials. We term these disturbances as anomalies, and pose the problem statement: if it is possible to also predict these anomalous events (proactive), and not just detect (reac...

The Madden-Julian Oscillation (MJO) is a tropical atmospheric phenomenon, associated with periods of active convection in the eastern hemisphere tropics.The MJOs temporal scale (22-90 days) coincides with a gap between weather (synoptic forecasts out to 10 days) and climate (seasonal and longer forecasts).Analysis of 35 years of daily rainfall data shows significant modulation of tropical and e...

Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract ...

In recent years, electronic vessel tracking has provided abundant data on vessel movements to surveillance authorities. Researchers have begun looking at the use of this data for anomaly detection using a wide variety of data mining techniques. Here we tackle anomaly detection with Bayesian Networks, training them with real world AIS data and producing models at two different time scales — both...