The inherent benefits of an accident prevention program are generally known only after an accident has occurred. The purpose of implementation of the program is to minimize the number of accidents and cost of damages. Allocation of resources to implement accident prevention program is vital because it is difficult to estimate the extent of damage caused by an accident. Accurate fatal accident predictions can provide a meaningful data that can be used to implement accident prevention program in order to minimize the cost of accidents. This paper forecast the fatal accidents of factories in India by using Auto-Regressive Integrating Moving Average Method (ARIMA) model. Accident data for the available period 1980 to 2013 was collected from the Labour bureau, Government of India to analyze the long term forecasts. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (0, 0, 1) is suitable model for prediction of fatal injuries. The number of fatal accidents is forecasted for the period 2014 to 2019. These results suggest that the policy makers and the Indian labour ministry must focus attention toward increasing fatal accidents and try to find out the reasons. It is also an opportunity for the policy makers to develop policies which may help in minimizing the number fatal accidents.
Miriam Fendeková, Pavla Pekárová, Marián Fendek, Ján Pekár and Peter Škoda
Changes in runoff parameters are very important for Slovakia, where stream-flow discharges, being supplied by precipitation and groundwater runoff, are preferentially influenced by climatic conditions. Therefore, teleconnections between runoff parameters, climate parameters and global atmospheric drivers such as North Atlantic Oscillation, Southern Pacific Oscillation, Quasi-biennial oscillation and solar activity were studied in the Nitra River Basin, Slovakia. Research was mostly based on records of 80 years (1931-2010) for discharges and baseflow, and 34 years for groundwater heads. Methods of autocorrelation, spectral analysis, cross-correlation and coherence function were used. Results of auto- correllograms for discharges, groundwater heads and base flow values showed a very distinct 11-year and 21-year periodicity. Spectrogram analysis documented the 11-year, 7.8-year, 3.6-year and 2.4-year periods in the discharge, precipitation and air temperature time series. The same cycles except of 11-years were also identified in the long-term series of the North Atlantic Oscillation and Southern Pacific Oscillation indices. The cycle from approximately 2.3 to 2.4-years is most likely connected with Quasi-biennial oscillation. The close negative correlation between the North Atlantic Oscillation winter index and the hydrological surface and groundwater parameters can be used for their prediction within the same year and also for one year in advance.