A REVIEW ON TROPICAL CYCLONES

In this review, we have discussed the important recent theoretical research works on tropical cyclone dynamics. For mitigation of the devastating effect of tropical cyclones on coastal human civilization more and more advanced forecasting techniques are evolving nowadays with the increase in the frequency of generation of tropical cyclones. Thus it is of utmost necessity to understand the anatomy and physiology of the dynamics of tropical cyclones. So researchers explain the cyclonic system from a different point of view and that is highlighted in this review. So this review illustrates, in brief, some important developed models.

Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting Method

A newly introduced method called Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting method is applied and extended in this study to forecast numerical values. Unlike traditional forecasting techniques which forecast only future values, our proposed method provides a new extension to correct the predicted values which is done by forecasting the estimated error. Experimental results demonstrated that the proposed method has a high accuracy both in training and testing data and outperform the state-of-the-art RNN models on Mackey-Glass, NARMA, Lorenz and Henon map datasets.

Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting Method

A newly introduced method called Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting method is applied and extended in this study to forecast numerical values. Unlike traditional forecasting techniques which forecast only future values, our proposed method provides a new extension to correct the predicted values which is done by forecasting the estimated error. Experimental results demonstrated that the proposed method has a high accuracy both in training and testing data and outperform the state-of-the-art RNN models on Mackey-Glass, NARMA, Lorenz and Henon map datasets.

Study of synoptic systems associated with intraseasonal variability of summer monsoon – A New Perspective

An attempt has been made in this paper to identify the important synoptic situations that result in widespread rainfall activity in Maharashtra and Gujarat based on latest observational technology and develop forecasting techniques for day to day short range prediction of monsoon activity in these areas with special reference to Monsoon '96.

Sortie-based aircraft component demand rate to predict requirements

PurposeForecasting techniques improve supply chain resilience by ensuring that the correct parts are available when required. In addition, accurate forecasts conserve precious resources and money by avoiding new start contracts to produce unforeseen part requests, reducing labor intensive cannibalization actions and ensuring consistent transportation modality streams where changes incur cost. This study explores the effectiveness of the United States Air Force’s current flying hour-based demand forecast by comparing it with a sortie-based demand forecast to predict future spare part needs.Design/methodology/approachThis study employs a correlation analysis to show that demand for reparable parts on certain aircraft has a stronger correlation to the number of sorties flown than the number of flying hours. The effect of using the number of sorties flown instead of flying hours is analyzed by employing sorties in the United States Air Force (USAF)’s current reparable parts forecasting model. A comparative analysis on D200 forecasting error is conducted across F-16 and B-52 fleets.FindingsThis study finds that the USAF could improve its reparable parts forecast, and subsequently part availability, by employing a sortie-based demand rate for particular aircraft such as the F-16. Additionally, our findings indicate that forecasts for reparable parts on aircraft with low sortie count flying profiles, such as the B-52 fleet, perform better modeling demand as a function of flying hours. Thus, evidence is provided that the Air Force should employ multiple forecasting techniques across its possessed, organically supported aircraft fleets. The improvement of the forecast and subsequent decrease in forecast error will be presented in the Results and Discussion section.Research limitations/implicationsThis study is limited by the data-collection environment, which is only reported on an annual basis and is limited to 14 years of historical data. Furthermore, some observations were not included because significant data entry errors resulted in unusable observations.Originality/valueThere are few studies addressing the time measure of USAF reparable component failures. To the best of the authors’ knowledge, there are no studies that analyze spare component demand as a function of sortie numbers and compare the results of forecasts made on a sortie-based demand signal to the current flying hour-based approach to spare parts forecasting. The sortie-based forecast is a novel methodology and is shown to outperform the current flying hour-based method for some aircraft fleets.

A Survey on Electric Power Demand Forecasting

Recently there has been tremendous change in use of the forecasting techniques due to the increase in availability of the power generation systems and the consumption of the electricity by different utilities. In the field of power generation and consumption it is important to have the accurate forecasting model to avoid the different losses. With the current development in the era of smart grids, it integrates electric power generation, demand and the storage, which requires more accurate and precise demand and generation forecasting techniques. This paper relates the most relevant studies on electric power demand forecasting, and presents the different models. This paper proposes a novel approach using machine learning for electric power demand forecasting.