Background: Short term current prediction for operational purposes is commonly carried out with the help of numericalocean circulation models. The numerical models have advantage that they are based on the physics of the underlying process. However because of their spatial nature they may not be so accurate while making stationspecific predictions. In such cases data-driven approaches like artificial neural network (ANN)'s trained on the basis of location-specific data may work better. In this paper an attempt is made to do daily predictions of ocean currents by combination of a numerical model and ANNs. Results:The difference in the current velocity estimated by the numerical model and actual observations at a giventime was calculated and corresponding error time series was formed based on all past numerical estimations and observations. An ANN was trained over such time series to predict errors for future, which were added to the numerical estimation so as to predict daily current velocities over multiple days in future. Conclusions:The suggested approach, implemented at two locations in Indian Ocean, was found to perform satisfactory current predictions up to a lead time of 5 days, as ascertained through various error statistics. The standalone networks once trained using the numerical outcome can reproduce such output well over future time without using variety of data and computational resources required for running the numerical model on a continuous basis.
For construction and mining activities, excavation is often performed with the help of blasting in hard or weathered rock or even hard soil. As a safety practice, the blasting operation is designed so that the nearby structures are not adversely affected. For this, blast vibration attenuation relationship for the ground media becomes necessary. In India, the relevant IS code specifies an attenuation relationship (Power expression) between the peak particle velocity, the charge weight and the distance of the monitoring point from the blast. However, the empirical coefficients are provided in IS code for only two categories-hard rock and weathered rock / soil. Hence they result in uneconomical and sometimes unviable blasting design. In lieu of above, site specific attenuation relationship may be used for design of blasting operation. Industrial practice is to find the empirical constants for the same Power expression (as IS code) using trial blast data from the site. For this purpose, the same dataset is used for parameter estimation as well as evaluation of the suitability of the estimated parameters. In this study it is demonstrated that evaluation of performance with a different dataset alters the conclusions. Further, expressions other than the commonly adopted Power expression might be more suitable for the relationship. In this article, two other expressions, namely, Reciprocal expression and Weibull model were identified which could be equally good. Exponents for scaled distance calculation, other than the popularly adopted 0.5, were found to be applicable in the case study. Trail blast data from a site was used to demonstrate the same. It was concluded that while developing site specific attenuation relationship, various expressions with different exponents should be examined for their suitability. The available dataset should be split into parameter estimation set and performance evaluation set. The best expression identified for the site from the performance evaluation set should be finally adopted for further activities.
Analysis of trend of epidemiological data helps to appreciate the progression of an epidemic and to develop monitoring and control strategies by the government agencies. Sen's Innovative Method suggests a graphical analysis, which can overcome many limitations of data such as short length, non-Gaussian nature, skewness or serial correlation. In this article, this method is applied for the first time on epidemiological data. For the case study, Covid-19 or SARS-CoV-2 data from India were employed. The results show that Sen's Innovative Method is capable of indicating the shift in epidemiological trend quite efficiently, before it is reflected in the time series or moving average plots. The graphical analysis worked particularly well in comparing the trends of monthly data. It is concluded that this method would be especially suitable for monitoring the epidemiological trend by breaking up the data into smaller segments, as was illustrated in the study.
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