To begin a zero accident campaign for industry, the first thing is to estimate the industrial accident rate and the zero accident time systematically. This paper considers the social and technical change of the business environment after beginning the zero accident campaign through quantitative time series analysis methods. These methods include sum of squared errors (SSE), regression analysis ...

Studies on safety in aviation are necessary for the development of new technologies to forecast and prevent aeronautical accidents incidents. When predicting these occurrences, literature frequently considers internal characteristics operations, such as aircraft telemetry flight procedures, or external characteristics, meteorological conditions, with only few relationships being identified betw...

Traffic accident forecasting is of great importance to urban public safety, emergency treatment, and construction planning. However, it very challenging since traffic accidents are affected by multiple factors, have multi-scale dependencies on both spatial temporal dimensional features. Meanwhile, rare events, which leads the zero-inflated issue. Existing methods cannot deal with all above prob...

We present a data-driven and physics-informed algorithm for drilling accident forecasting. The core machine-learning uses the data from telemetry representing time-series. have developed Bag-of-features representation of time series that enables to predict probabilities six types accidents in real-time. model is trained on 125 past 100 different Russian oil gas wells. Validation shows can forec...

Ocean forecasting and oil spill modelling and tracking are complex activities requiring specialised institutions. In this work we present a lighter solution based on the Operational Ocean Forecast Python Engine (OOFε) and the oil spill model General NOAA Operational Modelling Environment (GNOME). These two are robust relocatable and simple to implement and maintain. Implementations of the opera...

In view of the fact that oil spill remote sensing could only generate the oil slick information at a specific time and that traditional oil spill simulation models were not designed to deal with dynamic conditions, a dynamic data-driven application system (DDDAS) was introduced. The DDDAS entails both the ability to incorporate additional data into an executing application and, in reverse, the ...