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    Ionospheric TEC Forecasting over an Indian Low Latitude Location Using Long Short-Term Memory (LSTM) Deep Learning Network
    (2022-11-01)
    Reddybattula, Kanaka Durga
    ;
    Nelapudi, Likhita Sai
    ;
    Moses, Mefe
    ;
    Devanaboyina, Venkata Ratnam
    ;
    Ali, Masood Ashraf
    The forecasting of ionospheric electron density has been of great interest to the research scientists and engineers’ community as it significantly influences satellite-based navigation, positioning, and communication applications under the influence of space weather. Hence, the present paper adopts a long short-term memory (LSTM) deep learning network model to forecast the ionospheric total electron content (TEC) by exploiting global positioning system (GPS) observables, at a low latitude Indian location in Bangalore (IISC; Geographic 13.03° N and 77.57° E), during the 24th solar cycle. The proposed model uses about eight years of GPS-TEC data (from 2009 to 2017) for training and validation, whereas the data for 2018 was used for independent testing and forecasting of TEC. Apart from the input TEC parameters, the model considers sequential data of solar and geophysical indices to realize the effects. The performance of the model is evaluated by comparing the forecasted TEC values with the observed and global empirical ionosphere model (international reference ionosphere; IRI-2016) through a set of validation metrics. The analysis of the results during the test period showed that LSTM output closely followed the observed GPS-TEC data with a relatively minimal root mean square error (RMSE) of 1.6149 and the highest correlation coefficient (CC) of 0.992, as compared to IRI-2016. Furthermore, the day-to-day performance of LSTM was validated during the year 2018, inferring that the proposed model outcomes are significantly better than IRI-2016 at the considered location. Implementation of the model at other latitudinal locations of the region is suggested for an efficient regional forecast of TEC across the Indian region. The present work complements efforts towards establishing an efficient regional forecasting system for indices of ionospheric delays and irregularities, which are responsible for degrading static, as well as dynamic, space-based navigation system performances.
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    Inventory Management for Consumer Products: A Distribution Center in Thailand
    (2022-01-01)
    Leerojanaprapa, Kanogkan
    ;
    Bhundarak, Komn
    ;
    Atthirawong, Walailak
    ;
    Sirikasemsuk, Kittiwat
    The objective of the study is to formulate an appropriate order quantity policy. A best-selling product, a cleaner fresher mouth, was selected to be a representative of this study. Monthly Sales data of the cleaner fresher product was collected from January 2013 to December 2021, for 9 years, to forecast the sales of each product. Two forecasting techniques i.e. Exponential Smoothing and Box-Jenkins methods were compared to select the appropriate forecasting method by using MSE criterion. It was found that the Triple Exponential Method was the suitable forecasting model to forecast demand for the cleaner fresher products. These forecast values were then applied in inventory planning to determine the order policy by using Economic Order Quantity (EOQ) method of inventory management. The results found that EOQ technique resulted in a reduction in total cost rather than the company's current technique by 27.80%•