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• Open Access   Article

  Coupled WRF-AERMOD modeling system by using Dispersion of Air Pollutant and Generation of Gridded Emission Inventory of NOX over Faridabad and Gurugram

  Neelam Sharma, Ravendra Singh, Yogendra K. Rojoria, P. Rajendra, Rahul Boadh

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.8 , Issue.1 , pp.1-12, Feb-2021

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  Abstract
  Truthful identification of worsened Air Quality (AQ) regions in urban areas is very supportive in preparation healthier environmental performs and justification determinations. In current study, an effort has been prepared to classify the NOX diffusion configurations due to vehicular and industrialized foundations finished quickly increasing urban metropolises, Faridabad and Gurugram, India, during 2017. A dispersion model AERMOD with unmitigated exhibit of planetary boundary layer (PBL) elements and disconnected coupled closed a high-level coupler component through a high-goal mesoscale model WRF-ARW for reproducing the dissemination plans of NOX is reused for current study. The meteorological variables achieve by employing WRF-ARW model (two PBL schemes viz., non-local YSU and ACM2) are considered for integrating AERMOD. The high resolution 1 km x 1 km Gridded Emission Inventory (GEI) has been established by using the emission factors of different vehicles for Faridabad and Gurugram. Expressively, changed diffusion configurations of NOX have been observed among winter, summer, monsoon and post monsoon representing months. The computer-produced NOX focus is validated with existing perception checking stations of Central Pollution Control Board (CPCB), India. Measurable assessment uncovers that, the non-local YSU plot with AERMOD has demonstrated better execution contrast with ACM2 PBL plan of WRF-ARW model. The decayed air quality destinations are perceived over both investigation regions grounded on the validated model reconciliation of NOX focuses. It has supported by current investigation that, the viability of the set up GEI of NOX with coupled WRF-AERMOD demonstrating framework for air quality appraisal over the study region.

  Key-Words / Index Term
  AERMOD, Gridded Emission Inventory, Air Quality, WRF-ARW, Planetary Boundary Layer

  References
  [1] Dockery, D. W., C. A. Pope, X. Xu, J. D. Spengler, J. H. Ware, M. E. Fay, B. G. Ferris, & Speizer, F. E. “An association between air pollution and mortality in six U.S. cities”, N. Engl. J. Med., Vol. 329, Issue 24, pp. 1753–1759, 1993. doi:10.1056/NEJM199312093292401.
  [2] Intergovernmental Panel on Climate Change, Summary for policymakers, in Climate Change, “The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change”, Cambridge Univ. Press, New York, pp. 1–18, 2007.
  [3] Ramanathan, V., & Feng, Y., “Air pollution, greenhouse gases and climate change: Global and regional perspective”, Atmos. Environ, Vol. 43, pp. 37–50, 2009. doi:10.1016/j.atmosenv.2008.09.063.
  [4] Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., Briggs, J. M. “Global change and the ecology of cities”, Science, Vol. 319, Issue 5864, pp. 756–760, 2008.doi:10.1126/science.1150195.
  [5] Wilks, D. S., “Statistical Methods in the Atmospheric Science”, Elsevier Academic Press, U.S.A, pp 627, 2008.
  [6] Tiwari, A. Rajoria, YK., Boadh, R. and Singh R. “Analysis of Average Rainfall Super Cyclone by using Double Integration Technique”, Int. J. Sci. Res. in Mathematical and Statistical Sciences, Vol. 7, Issue 2,, pp. 82-85, 2020.
  [7] ARAI, Draft report on “Emission Factor development for Indian Vehicles” as a part of Ambient Air Quality Monitoring and Emission Source Apportionment Studies. pp. 1-94, 2007.
  [8] WHO, “Quantifying Selected Major Risks to Health, Chapter 4 from the World Health Report, World Health Organization”, Geneva, 2002.
  [9] Gurjar, B. R., Butler, T. M., Lawrence, M. G., & Lelieveld, J., “Evaluation of emissions and air quality in megacities”, Atmospheric Environment, Vol. 42, pp. 1593–1606, 2008.
  [10] Cimorelli, A. J., Perry, G. S., Venkatram, A., Weil, J. C., Paine, R. J., Wilson, R. B., Lee, R. F., Peters, W. D., Brode, R. W. “AERMOD: A Dispersion Model for Industrial Source Applications. Part 1: General model formulation and boundary layer characterization”. Journal of Applied meteorology, Vol. 44, pp. 682-693, 2005.
  [11] Perry, S. G., Cimorelli, A. J., Paine, R. J., Brode, R. W., Weil, J. C., Venkatram, A., Wilson,R. B., Lee, R. F., Peters, W. D., “AERMOD: a dispersion model for industrial source applications. Part II: model performance against 17 field study databases”, Journal of Applied Meteorology, Vol. 44, pp. 694–708, 2005.
  [12] Kesarkar, A. P., Dalvi, M., Kaginalkar, A., Ojha, A. “Coupling of the Weather Research and Forecasting Model with AERMOD for pollutant dispersion modeling, A case study for PM10 dispersion over Pune, India”, Atmospheric Environment, Vol. 41, pp. 1976–1988, 2007.
  [13] Gurjar, B. R., van Aardenne, J. A., Lelieveld, J., & Mohan, M,. “Emission estimates and trends (1990–2000) for mega city Delhi and implications”, Atmospheric Environment, Vol. 38, pp. 5663–5681, 2004.
  [14] Mohan, M., Bhati, S., Rao, A. (2011). Application of air dispersion modeling for exposure assessment from particulate matter pollution in Mega City Delhi, Asia-Pac J Chem Eng 6 (2011) 85–94
  [15] Goyal, P., Mishra, D., & Kumar, A. “Vehicular emission inventory of criteria pollutants in Delhi”, Springer Plus, Vol. 2, Issue 216, 2013.
  [16] Carbonell, L. T., Mastrapa, G. C., Rodriguez, Y. F., Escudero, L. A., Gacita, M. S., Morlot, A. B., Montejo, I. B., Ruiz, E. M., Rivas, S. P. “Assessment of the Weather Research and Forecasting model implementation in Cuba addressed to diagnostic air quality modeling”, Atmospheric Pollution Research, Vol. 4, pp. 64?74, 2013.
  [17] Kumar, A., Patil, R. S., Dikshit, A. K., Kumar, R., “Application of WRF Model for Air Quality Modelling and AERMOD – A Survey”, Aerosol and Air Quality Research, Vol. 17, pp. 1925–1937, 2017.
  [18] Srinivas, C. V., Venkatesan, R., Baskaran, R., Rajagopal, V., Venkatraman, B., “Regional scale atmospheric dispersion simulation of accidental release of radionuclides from Fukushima Dai-ichi reactor”, Atmospheric Environment, Vol. 61, pp. 66–84, 2012
  [19] Srikanth, M., Satyanarayana, A. N. V., Srinivas, C.V., and Kumar, M., “Mesoscale atmospheric flow-field simulations for air quality modelling over complex terrain region of Ranchi in eastern India using WRF”, Atmospheric Environment, Vol. 107, pp. 315-328, 2015.
  [20] Wolf, T., Pettersson, L. H. and Esau, I. “A very high-resolution assessment and modelling of urban air quality”, Atmos. Chem. Phys., Vol. 20, pp. 625–647, 2020. DOI: https://doi.org/10.5194/acp-20-625-2020
  [21] Mentesea, S., Miricib, N.A., Elbirc, T., Tuygunc, G. T., Bakard, C., Otkune, M. T., and Oymakd, S., “A comprehensive assessment of ambient air quality in Çanakkale city: Emission inventory, air quality monitoring, source apportionment, and respiratory health indicators”, Atmospheric Pollution Research, Vol. 11, Issue 12, pp. 2282-2296, 2020.
  [22] Boadh, R., Satyanarayana, A. N. V., Rama Krishna, T. V. B. P. S., and Madala, S. “Sensitivity of PBL Parameterization schemes of Weather Research Forecasting Model and integration with AERMOD in the Dispersion of NOX over a Coastal Region of Southern Visakhapatnam (India)”. Asia-Pacific Journal of chemical engineering, 2015. DOI: 10.1002/apj.(1876).
  [23] Boadh, R., Satyanarayana A. N. V., Rama Krishna, T. V. B. P. S., and Madala, S. “Sensitivity of PBL schemes of WRF-ARW Model in Simulating Boundary Layer Flow Parameters for its Application to Air pollution Dispersion Modeling over a Tropical Station”, Atmosfera, Vol. 29, Issue 1, pp. 61-81, 2016.
  [24] Sharma, N., Boadh, R. and Singh, R., “Customization of Weather Research Forecast (WRF) model by conducting the Numerical Simulation and sensitivity experiment of Planetary Boundary Layer (PBL) parameterization schemes over Gurugram”, European Journal of Molecular and Clinical Medicine, Vol. 07, Issue 9, pp. 892-914, 2020.
  [25] Baker, K. R., Misenis C., Obland, M. D., Ferrare, R. A., Scarino, A. J., and Kelly, J. T., “Evaluation of surface and upper air ?ne scale ARW meteorological modeling of the May and June 2010 CalNex period in California”, Atmospheric Environment, Vol. 80, pp. 299-309, 2013.
  [26] Boadh, R., Satyanarayana A. N. V., Rama Krishna, T. V. B. P. S., Subba Rao, A., Rajendra P. “Numerical simulation of boundary layer flow parameters by using wrf-arw model over a tropical region”, Jr. of Industrial Pollution Control, Vol. 33, Issue 1, pp. 1148-1154, 2017.
  [27] Sharma N., Boadh, R. and Singh R., “Assessment of the ambient air quality during Diwali festival over Faridabad city - a case study”, Jr. of Industrial Pollution Control, Vol. 34, Issue, 2, pp. 2198-2205, 2018.
  [28] Cimorelli, A. J., Perry, S. G., Venkatram, A., Weil, J. C., Paine, R. J., Wilson, R. B., Lee, R. F., Peters, W. D., Brode, R. W., Paumier, J. O. “AERMOD: Description of model formulation. US Environmental Protection Agency”, EPA Report No. 454/R-03-002d, pp. 85-86, 2004.
  [29] CPCB (Central Pollution Control Board), India. “National Ambient Air Quality Standards (NAAQS)”. Gazette notification, New Delhi, 2009.
  [30] NCEP-DSS1, “NCEP Global Tropospheric Analyses, (1 X 1 deg), from Data Support Division (DSS), NCAR, Dataset # 083.2. /http://dss.ucar.edu/datasets/ds083.2/, 2005.
  [31] Kandlikar, M., & Ramachandran, G., “The causes and consequences of particulate air pollution in urban India: a synthesis of the science”, Annual Review of Energy and the Environment, Vol. 25, pp. 629–684, 2000.
  [32] Boadh, R., Satyanarayana, A. N. V., Rama Krishna, T. V. B. P. S. “Assessment of Dispersion of Oxide of Nitrogen using AERMOD Model over a Tropical Industrial Region”, International Journal of Computer Applications, Vol. 90, Issue 11, pp. 43-50, 2014. doi: 10.5120/15768-4464.
  [33] Skamarock, W. C., Weisman, M. L., “The impact of positive-definite moisture transport on NWP precipitation forecasts”, Monthly Weather Review, Vol. 137, pp. 488-494, 2009.
  [34] Spiridonov, V., Jakimovski, B., Spiridonova, I., and Pereira, G., “Development of air quality forecasting system in Macedonia, based on WRF-Chem model, Air Quality”, Atmosphere & Health, Vol. 12, pp. 825–836, 2019.
  [35] Chen F. and J. Dudhia, “Coupling an advanced land-surface/ hydrology model with the Penn State/ NCAR MM5 modeling system. Part I: Model description and implementation”. Mon. Weather Rev., Vol. 129, pp. 569-585, 2001.
  [36] Dudhia, J. “Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model”, J Atmos Sci, Vol. 46, pp. 3077–3107, 1989.
  [37] Kain, J. S., “The Kain–Fritsch Convective Parameterization an Update”, Journal of Applied Meteorology, Vol. 43, Issue 1, pp. 170–181, 2004.
  [38] Ferrier, B. S., Lin, Y., Black, T., Rogers, E., DiMego, G. “Implementation of a new grid-scale cloud and precipitation scheme in the NCEP Eta model”, Proceedings of the 15th Conference on Numerical Weather Prediction; San Antonio, Tex, USA, American Meteorological Society, pp. 280–283, 2002.
  [39] Hong, S. Y., Noh, Y., Dudhia, J., “A new vertical diffusion package with explicit treatment of entrainment processes”, Mon. Wea Rev, Vol. 134, pp. 2318-2341, 2006.
  [40] Pleim, J.E. “A combined local and nonlocal closure model for the atmospheric boundary layer, Part I model description and testing”, J Appl Meteorol Climatol, Vol. 46, Issue 9, pp. 1383–1395, 2007.
  [41] Chang, J. C., & Hanna, S. R. “Air quality model performance evaluation”, Meteorol Atmos Phys, Vol. 87, pp. 167–196, 2004. DOI 10.1007/s00703-003-0070-7.
  [42] Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J., and Clough, S.A., “Radiative transfer for inhomogeneous atmosphere RRTM, a validated correlated-k model for the longwave”, J Geophys Res, Vol. 102, Issue D14, pp. 16663–16682, 1997.
  [43] Arya, S.P.S., “Air Pollution Meteorology and Dispersion”, Oxford University Press, New Yrok, pp. 310-311, 1999.
  [44] Harrison, R. M., “An introduction to pollution science”, pp. 29, 2006.
  [45] Rao, M. N., & Rao, H. V. N., “Air pollution”, ISBN 0-07-451871-8, pp. 15, 1989.

  Citation
  Neelam Sharma, Ravendra Singh, Yogendra K. Rojoria, P. Rajendra, Rahul Boadh, "Coupled WRF-AERMOD modeling system by using Dispersion of Air Pollutant and Generation of Gridded Emission Inventory of NOX over Faridabad and Gurugram," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.8, Issue.1, pp.1-12, 2021

• Open Access   Article

  The Future of African Financial Markets: Modelling in Traditional and Machine Learning Techniques

  A.A. Agyemang-Badu, I.N. McSeplus

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.8 , Issue.1 , pp.13-26, Feb-2021

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  Abstract
  The opportunities, prospects and challenges of Africa is frequently been trumpeted by several scholars and experts. This study examines African Financial Markets using both traditional modelling techniques and machine learning tools. We selected proxy countries based on the macroeconomic factors and stock markets analyzed. The study revealed that even though traditional modelling can be used for modelling time series, it can significantly be improve by machine learning tools. The results show that Africa stock markets are profitably but have some underlying risk that is linked to the economy of the African economies. We highlight the weakness in the macroeconomic factors such as weakness in managing inflation, high borrowing rate, increased interests rate as well as slow growth in FDI and GDP. We recommend swift policy measures to deal with these weaknesses.

  Key-Words / Index Term
  Africa, Macroeconomic factors, Fast Fourier Transformation, Principal Component Analysis, COVID-19

  References
  [1] G. Boako and P. Alagidede, “Co-movement of Africa’s equity markets: Regional and global analysis in the frequency-time domains” Physica A., Vol. 468, pp. 359-380, 2017.
  [2] African Development Bank, “African Economic Outlook 2020: Developing Africa’s Workforce for the Future”. 2020.
  [3] International Finance Corporation, “Shaping the Future of Africa Markets and Opportunities for Private Investors”, Washington: IFC. 2018.
  [4] A. Allen, I. Otchere and L. Sebbet, “African Financial Systems: A Review” Review of Development Finance 1, pp.79-113, 2011.
  [5] African Development Bank, “African Economic Outlook 2020: Supplement Amid COVID-19”, 2020.
  [6] African Development Bank, “African Economic Outlook 2020: Developing Africa’s Workforce for the Future” 2020.
  [7] L. Senbet and I. Otchere, “African Stock Markets: Ingredients for Development and Capacity Building” Edited Q. Marc and V. Genevieve, Palgrave (Eds.) Macmillan Publishing, in African Finance in the 21th Century 2010.
  [8] G. Gondwe “Assessing the Impact of COVID-19 on Africa’s Economic Development” UNCTAD/ALDC/MISC/2020/3 2020.
  [9] G. E. P. Box and G. M. Jenkins, “Time Series Analysis: Forecasting and Control”, Second ed. Holden-Day, San Francisco 1976.
  [10] H. Tong, “Non-linear Time Series”, Oxford University Press, Oxford 1990.
  [11] L. Xiao and A. Aydemir, “Volatility modelling and forecasting in finance”, John Knight, Stephen Satchell, In Quantitative Finance, Forecasting volatility in the financial markets (Third Edition), Butterworth-Heinemann, pp. 1-45 2007.
  [12] R. F. Engle, “Autoregressive conditional heteroscedasticity with estimates of variance of UK inflation”, Econometrica, Vol. 50, pp. 987-1008. 1982.
  [13] T. Bollerslev, “Generalized autoregressive conditional heteroscedasticity”, Journal of Econometrics, Vol. 31, pp. 307-327, 1986.
  [14] S. J. Taylor, “Modelling Financial Time Series”, Chichester: John Wiley, 1986.
  [15] C. W. J. Granger, “Some Properties of Time Series Data and their Use in Econometric Model Specification”, Journal of Econometrics, Vol. 16, pp. 121-130, 1981.
  [16] C. W. J. Granger and A. A. Weiss, “Time series analysis of error-correcting models”, Edited: S. Karlln, eds., Studies in econometrics, time series, and multivariate analysis, Academic Press, New York pp. 255-278, 1983.
  [17] R. F. Engle and C. W. J. Granger, “Cointegration and Error-Correction: Representation, Estimation and Testing”, Econometrica, Vol. 55, pp. 251-279, 1987.
  [18] S. Johansen, “Statistical analysis of cointegration vectors”. Journal of economic dynamics and control, Vol. 12 No. 2-3, pp. 231-254, 1988.

  Citation
  A.A. Agyemang-Badu, I.N. McSeplus, "The Future of African Financial Markets: Modelling in Traditional and Machine Learning Techniques," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.8, Issue.1, pp.13-26, 2021

• Open Access   Article

  Viscous Dissipation, Internal Heat Generation (Absorption) effects on Free Convection Flow about a Vertical Flat Plate with an applied Magnetic field

  A.H. Srinivasa, Ajay C.K.

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.8 , Issue.1 , pp.27-31, Feb-2021

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  Abstract
  A numerical technique has been carried out to describe the boundary layer flow and heat transfer on a free convection flow about a vertical flat plate in the presence of Viscous dissipation, Internal heat generation (absorption) along with MHD. Reasonable change is used to form a system of coupled non linear partial differential equations for governing both the flow and heat transfer. These conditions have been settled mathematically by using an implicit finite difference strategy along with quasilinearization technique. The measured numerical results expressed here are addressed, discussed and described in terms of temperature distribution and velocity distribution for various values of the magnetic field, Prandtl number, internal heat generation (absorption) and viscous dissipation,which are of physical and engineering interest.

  Key-Words / Index Term
  Convection, Internal heat generation, MHD, Prandtl number, Viscous dissipation

  References
  [1] B. Gebhart, “Effects of viscous dissipation in natural convection”, J. Fluid Mech. 14,pp 225–232,1962.
  [2] A. Pantokratoras “Effect of viscous dissipation in natural convection along a heated vertical plate”, Applied Mathematical Modelling Vol 29, pp 553–564,2005.
  [3] Mohammed A.Mohammed Ahmedn, Mohammed E.Mohammed, Ahmed A.Khidir, “On linearization method to MHD boundary layer convective heat transfer with low pressure gradient”, Propulsion and Power Research.,Vol 4(2),pp 105–113,2013.
  [4] G. Palani, A. Arutchelvi, “Heat transfer effects on free convection viscous dissipation flow past an inclined plate”, AIP Conference Proceedings, 2112, 020055, 2019.
  [5] A.J. Chamka, H.S.Takhar , G. Nath, “Mixed convection flow over a vertical plate with localized heating (cooling), magnetic field and suction (injection)”, Heat and Mass Transfer, 40, pp 835-841, 2004.
  [6] R. Nandkeolyar, M. Narayana, S. S. Motsa, P. Sibanda , “Magnetohydrodynamic mixed convective flow due to a vertical plate with induced magnetic field”, J. Thermal Sci. Eng. Appl, 10(6). 061005 (11 pages), 2018.
  [7] O.D. Makinde.P. Sibanda, “Magneto hydrodynamic mixed convective flow and heat and mass transfer past a vertical plate in a porous medium with constant wall suction”, Article?in?Journal of Heat Transfer, 130(11),112602 (8 pages), 2008 .
  [8] Rashidi MM, Kavyani N, Abelman S, Uddin MJ, Freidoonimehr N, “Double Diffusive Magnetohydrodynamic (MHD) Mixed Convective Slip Flow along a Radiating Moving Vertical Flat Plate with Convective Boundary Condition”, PLoS ONE, Vol 9(10), e109404, 2014.
  [9] R. Nandkeolyar, M. Narayana S. S. Motsa P. Sibanda, “Magnetohydrodynamic Mixed Convective Flow Due to A Vertical Plate With Induced Magnetic Field”, Journal of Thermal Science and Engineering Applications, 2017.
  [10] K. Bhattacharyya, S. Mukhopadhyay, G.C. Layek “MHD boundary layer slip flow and heat transfer over a flat plate”, Chin. Phys. Lett., Vol. 28 (2), pp. 024701, 2011.
  [11] Md. MamunMolla, Anita Biswas, Abdullah Al-Mamun & Md. Anwar Hossain, (2014), Natural Convection Flow along an Isothermal Vertical Flat Plate with Temperature Dependent Viscosity and Heat Generation, Journal of Computational Engineering, Article ID 712147,13.
  [12] A. H. Srinivasa, K.R. Jayakumar, A.T. Eswara. “Unsteady Natural Convection MHD Flow from a Porous Vertical Flat Plate with constant Heat Flux and Suction (Injection).” International Journal of Mathematical Sciences and Engineering. Vol.5, N0.2, pp 377-384, 2011.
  [13] C. K. Ajay, and A. H. Srinivasa Internal heat generation effects on mixed convection flow from a vertical flat plate AIP Conference Proceedings 2277, 030023 (2020);
  [14] Qammar Rubbab,Dumitru Vieru,Corina Fetecau,Constantin Fetecau, Natural Convection Flow near a Vertical Plate that Applies a Shear Stress to a Viscous Fluid, Plus one , 2013.
  [15] K. Inouye and A. Tate, Finite difference version of quasilinearization applied to boundary layer equations, A.I.A.A.J., 12, pp 558-560, 1974.
  [16] Ajay C K, A. H. Srinivasa, Unsteady two dimensional Accelerating Boundary Layer Flow over a Wedge with Temperature dependent Viscosity, International Journal of Engineering Research and Applications (IJERA), Vol. 09, No.07, pp. 33-38, 2019.
  [17] R. S. Varga, Matrix Iterative Analysis Prentice-Hall, New Jersy, 2000.

  Citation
  A.H. Srinivasa, Ajay C.K., "Viscous Dissipation, Internal Heat Generation (Absorption) effects on Free Convection Flow about a Vertical Flat Plate with an applied Magnetic field," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.8, Issue.1, pp.27-31, 2021

• Open Access   Article

  Univariate Modeling of Indonesian Economic Growth Movement Using Markov Switching in Mean – Autoregressive

  M. Fajar, Y.G. Winarti

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.8 , Issue.1 , pp.32-38, Feb-2021

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  Abstract
  The purpose of this research is to construct a model of the Indonesia economic growth movement. Data used is economic growth rate (year on year, in percent) from the 1983 1st quarter – 2020 3rd quarter. Data sourced is Statistics Indonesia. The model used to construct the economic growth movement is Markov Switching in Mean - Autoregressive (MSM-AR) Model. The results of this research are that MSM-AR(3) was able to fit the recession signal according to the period of the economic crisis in 1998 and the period of the COVID-19 pandemic. By using the MSM-AR(3) model, it can be predicted that in the 4th quarter of 2020, the economic condition is still in the recession regime because of the COVID-19 pandemic. In 2021, MSM-AR(3) predicts that there is an economic recovery. It is indicated by positive economic growth forecasting and increasing every quarter, and the probability of a recession regime reach more than 0.5 per quarter.

  Key-Words / Index Term
  Markov, switching, growth, transition, autoregressive

  References
  [1] H. Akaike, “A New Look at the Statistical Model Identification,” IEEE. Transaction on Automatic Control, AC-19, pp. 716-723, 1974.
  [2] N. Anand, T. Kumar, “Prediction of User Interest and Behaviour using Markov Model,” International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.119-123, 2017.
  [3] F.T. Boldeanu, L. Constantinescu, “The main determinants affecting economic growth,” Bulletin Transilvania. University of Brasov Series V: Economic Sciences, Vol. 8 Issue 2, pp. 329-338, 2015.
  [4] R. Dornbusch, S. Fischer, R. Startz, “Macroeconomics, Seventh Edition,” McGraw Hill, New York,1998.
  [5] U. Fritsche, V. Kuzin, “Prediction of Business Cycle Turning Point in Germany,” Jahrbucher fut Nationalokonomie und Statistik, Vol.225, Issue 1, pp. 22-43, 2005.
  [6] T.H. Goodwin, “Business Cycle Analysis with a Markov switching Model,” Journal of Business & Economic Statistics, Vol. 11 No. 3, pp. 331-339, 1993.
  [7] J.D. Hamilton, “Time series Analysis,” Princeton University Press, Princeton, 1994
  [8] J.D. Hamilton, “A New Approach to the Economic Analysis of Nonstationary time series and the business cycle,” Econometrica 57, pp. 357-384, 1989.
  [9] J.D. Hamilton, “Analysis of Time Series Subject to Changes in Regime,” Journal of Econometrics 45, pp. 39-70, 1990.
  [10] J.D. Hamilton, G.P. Quiroz, “What Do the Leading Indicators Lead?.” The Journal of Business, Vol.69, No.1, pp. 27-49, 1996.
  [11] J.D. Hamilton, “Regime-Switching Models (prepared for: Palgrave Dictionary of Economics),” 2005.
  [12] C.J. Kim, “Dynamic Linear Models with Markov-Switching,” Journal of Econometrics, Vol. 60, Issue 1, pp. 1-22, 1994.
  [13] C.J. Kim, R.N. Charles, “State-Space Models with Regime Switching,” The MIT Press, Cambridge, 1999.
  [14] S. Kuznets, “Modern economic growth: findings and reflections,” The American economic review Vol. 63, Issue 3, pp. 247-258, 1973.
  [15] A.P. Layton, M. Katsuura, M, “Comparison of Regime Switching, Probit, and Logit Models in Dating and Forecasting US Business Cycles,” International Journal of Forecasting, Vol. 17, Issue 3, pp. 59-70, 2001.
  [16] I. Medhioub, “A Markov Switching Three Regime Model of Tunisian Business Cycle,” American Journal of Economics Vol. 5, Issue 3, pp. 394-403, 2015.
  [17] E. Moolman, “A Markov Switching Regime Model of the South African Business Cycle,” Economic Modelling 21, pp. 631-646, 2004.
  [18] NBER, “US Business Cyce Expansion and Contractions,” USA, 2016.
  [19] T.G. Petturson, “Business Cycle Forecasting and Regime Switching,” Central Bank of Iceland Working Paper no.7, 2007.

  Citation
  M. Fajar, Y.G. Winarti, "Univariate Modeling of Indonesian Economic Growth Movement Using Markov Switching in Mean – Autoregressive," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.8, Issue.1, pp.32-38, 2021

• Open Access   Article

  Forecasting and Modeling Monthly Rainfall in Bengaluru, India: An Application of Time Series Models

  Hemlata Joshi, Deepa Tyagi

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.8 , Issue.1 , pp.39-46, Feb-2021

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  Abstract
  Rainfall is the most essential stochastic phenomenon which plays an important role in Indian agriculture sector and is necessary for economic growth of the country. These days, the prediction of rainfall has become a most challenging task as it is drastically affected by climate changes due to the worsen effects global warming. For the accurate and timely rainfall predictions, in this article seasonal Naive, triple exponential smoothing and seasonal ARIMA time series models have been applied and the comparison of accuracy of forecasts of these time series models has been checked through various scale dependent error forecast methods and the residual analysis. Further, the best fitted time series model for monthly prediction of rainfall of Bengaluru, Karnataka is suggested through the empirical analysis by utilizing the monthly rainfall data recorded from the years 2009 to 2018 of the Bengaluru city of Karnataka State of India and the results show that the seasonal autoregressive moving average model i.e. ARIMA(0,0,2)(1,1,1)_12 provides the more accurate results to forecast the rainfall pattern in Bangalore City of India than the other time series models.

  Key-Words / Index Term
  Rainfall Forecasting, Time Series Models, SARIMA, Seasonal Naive, Exponential Smoothing.

  References
  [1] A.R. Abdul-Aziz, M. Anokey, A. Kwame, L. Munyakazi and N.N.N. Nsowah-Nuamah, “ Modelling and forecasting rainfall pattern in Ghana as a seasonal arima process: The case of ashanti region ”, International Journal of humanities and Social Science, Vol. 3, No. 3, pp. 224-233, 2013.
  [2] R.S. Ajayamohan, W.J. Merryfield and V.V. Kharin, “ Increasing trend of synoptic activity and its relationship with extreme rain events over central India ”, Journal of Climate, Vol. 23, pp.1004-1013, 2010.
  [3] H. Annamalai, J. Hafner, K.P. Sooraj and P. Pillai, “Global warming shifts monsoon circulation drying south Asia ”, Journal of Climate, Vol. 26, pp. 2701-2718, 2013.
  [4] P. Anuchaivong, D. Sukawat, and A. Luadsong, “ Statistical downscaling for rainfall forecasts using modified constructed analog method in Thailand”, The Scientific World Journal, Vol.1 pp.24, 2017,
  [5] N.W. Arnell, “ Climate change and global water resources: SRES emissions and socio-economic scenarios ”. Global Environmental Change, Vol. 14, No. 1, pp. 31-52, 2004.
  [6] A. Asfaw, B. Simane, A. Hassen, A. Bantider, “Variability and time series trend analysis of rainfall and temperature in northcental Ethiopia: A case study in Woleka sub-basin ”, Weather and Climate Extremes , Vol.19, pp. 29-41, 2018.
  [7] D.A. Attah, G.M. Bankole, “Time series analysis model for annual rainfall data in Lower Kaduna Catchment Kaduna Nigeria ”, Global Journal of Researches in Engineering Civil and Structural Engineering, Vol. 11, pp. 1-7, 2011.
  [8] R.V. Cruz, H. Harasawa, M. Lal, S. Wu, Y. Anokhin, B. Punsalmaa, Y. Honda, M. Jafari, C. Li and N.N. Huu, “Asia climate change 2007: impacts, adaptation and vulnerability ”, Cambridge University Press, Cambridge, pp. 469-506, 2007.
  
  [9] S.K. Dash, M.A. Kulkarni, U.C. Mohanty and K. Prasad, “Changes in the characteristics of rain events in India”, Journal of Geophysics Res, pp. 114, 2009,
  [10] W.E. Easterling, “ Guidelines for adapting agriculture to climate change, Handbook of climate change and agroecosystems: Impacts, adaptation and migration, ICP series on climate change. Impact, Adaptation, and Migration ”, Imperial College Press, London, Vol. 1, pp. 269-287, 2011.
  [11] A. Graham, E.P. Mishra, “ Time series analysis model to forecast rainfall for Allahabad region ”, Journal of Pharmacognosy and Phytochemistry , Vol. 6, No. 5, pp. 1418-1421, 2017.
  [12] P. Guhathakurta, O.P. Sreejith and P.A. Menon, “ Impact of climate change on extreme rainfall events and flood risk in India ”, Journal of Earth System Science, Vol. 120, No. 3, pp. 359-373, 2011.
  [13] C. Ifejika Speranza, “Resilient adaptation and climate change in African agriculture ”, German Development Institute, Bonn, Germany, 2010.
  [14] IPCC, The physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp. 504-511, 2007.
  [15] S.K. Khadar Babu, K. Karthikeyan, M.V. Ramanaiah and D. Ramanah, “Prediction of rain-fall flow time series using auto- regressive models ”. Advances in Applied Science Research, Vol. 2, No. 2, pp. 128-133, 2011.
  [16] R.S. Kamath, R.K. Kamat, “ Time-series analysis and forecasting of rainfall at Idukki district, Kerala: Machine learning approach ”, Disaster Advances, Vol. 11, No. 11, pp. 27-33, 2018.
  [17] S.S. Kashid, R. Maity, “ Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using genetic programming ”, Journal of Hydrology, pp. 26-41, 2012.
  [18] Z.W. Kundzewicz, L.J. Mata, N.W. Arnell, P. Doll, B. Jimenez, K. Miller, T. Oki, Z. Sen, and I. Shiklomanov, “The implications of projected climate change for freshwater resources and their management ”, Hydrological Science Journal, Vol. 53, No. 1, pp. 3-10, 2008.
  [19] A. Kulkarni, “ Weakening of Indian summer monsoon rainfall in warming environment ”, Theoritical and Applied Climatology, Vol. 109, pp. 447-459, 2012.
  [20] T.S. Manepalli, C. Subramanian, “Time series analysis of large scale rainfall data using regression automata models ”, International Journal of Intelligent Engineering & System, Vol. 11, pp. 118-127, 2018.
  [21] A. Mondal, D. Khare and S. Kundu, “ Change in rainfall erosivity in the past and future due to climate change in the central part of India ”, International Soil and Water Conservation Research, Vol. 4, pp.186-194, 2016.
  [22] D.A. Mooley, B. Parthasarathy, “ Fluctuations in all-India summer monsoon rainfall during 1871-1978 ”, Climate change, Vol. 6, pp. 287-301, 1984.
  [23] P.E. Nail and M. Momani, “ Time series analysis model for rainfall data in Jordan: A case study using time series analysis ”, American Journal of Environmental Science, Vol. 5, No. 5, pp. 599-604, 2009.
  [24] T.O. Olatayo, A.L. Taiwo, “ Statistical modeling and prediction of rainfall time series data ”, Global Journal of Computer Science and Technology: G Interdisciplinary, Vol. 14, No. 1, pp. 2-10, 2014.
  [25] T. Papalaskaris, T. Panagiotidis and A. Pantrakis, “ Schotastic monthly rainfall time series analysis, modeling and forecasting in Kavala city, Greece, North-Eastern Mediterranean Basin ”, Procedia Engineering, Vol. 162, pp. 254-263, 2016.
  [26] M. Rajeevan, J. Bhate and A.K. Jaswal, “Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data ”, Geophysics Research Letters,Vol. 35, L18707, 2008.
  [27] A. Ranade, N. Singh, H.N. Singh and N.A. Sontakke, “On variability of hydrological wet season, seasonal rainfall and rain water potential of the river basins of India 1813-2006 ”, Journal of Hydrological research and Development, Vol. 23, pp. 79-108, 2008.
  [28] M. Sidiq, “ Forecasting rainfall with time series model ”, IOP Conf. Series: Materials Science and Engineering, pp. 1-7, 2018.
  [29] E.S. Uba, H.R. Bakari, “An application of time series analysis in modeling monthly rainfall data for Maiduguri, North Eastern Nigeria ”, Mathematical Theory and Modeling, Vol. 11, pp. 24-33, 2015.
  [30] T.H. Udayashankara, B.M. Sadashiva Murthy and M. Madhukar, “ Impact of climate change on rainfall pattern and reservoir level ”, Journal of Water Resource Engineering and Management, Vol. 3, No. 1, pp. 10-14, 2016.
  [31] F. Yasmeen, S. Hameed, “ Forecasting of rainfall in Pakistan via sliced functional time series (SFTS) ”, World Environment, Vol. 8, No. 1, pp. 1-14, 2018.

  Citation
  Hemlata Joshi, Deepa Tyagi, "Forecasting and Modeling Monthly Rainfall in Bengaluru, India: An Application of Time Series Models," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.8, Issue.1, pp.39-46, 2021

• Open Access   Article

  Bayesian Estimation for the Parameter of Gamma Lomax Distribution under Different Loss Functions

  Arun Kumar Rao, Himanshu Pandey

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.8 , Issue.1 , pp.47-51, Feb-2021

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  Abstract
  In this paper, the Gamma-Lomax distribution is considered for Bayesian analysis. The expressions for Bayes estimators of the parameter have been derived under squared error, precautionary, entropy, K-loss, and Al-Bayyati’s loss functions by using quasi and gamma priors.

  Key-Words / Index Term
  Bayesian method, Gamma-Lomax distribution, quasi and gamma priors, squared error, precautionary, entropy, K-loss, and Al-Bayyati’s loss functions.

  References
  [1] G.M. Cordeiro, E.M.M. Ortega, and B.V. Popovic, “The Gamma-Lomax distribution”, J. Stat. Comp. Sim. IFirst , DOI: 10.1080/00949655.2013.822869, 2013.
  [2] A. Zellner, “Bayesian estimation and prediction using asymmetric loss functions”, Jour. Amer. Stat. Assoc., 91, 446-451, 1986.
  [3] A.P. Basu and N. Ebrahimi, “Bayesian approach to life testing and reliability estimation using asymmetric loss function”, Jour. Stat. Plann. Infer., 29, 21-311991.
  [4] J.G. Norstrom, “The use of precautionary loss functions in Risk Analysis”, IEEE Trans. Reliab., 45(3), 400-403, 1996.
  [5] R. Calabria and G. Pulcini, “Point estimation under asymmetric loss functions for left truncated exponential samples”, Comm. Statist. Theory & Methods, 25 (3), 585-600, 1994.
  [6] D.K. Dey, M. Ghosh and C. Srinivasan, “Simultaneous estimation of parameters under entropy loss”, Jour. Statist. Plan. And infer., 347-363, 1987.
  [7] D.K. Dey and Pei-San Liao Liu, “On comparison of estimators in a generalized life Model”, Microelectron. Reliab. 32 (1/2), 207-221, 1992.
  [8] M.T. Wasan, “Parametric Estimation”, New York: Mcgraw-Hill, 1970.
  [9] Al-Bayyati, “Comparing methods of estimating Weibull failure models using simulation”, Ph.D. Thesis, College of Administration and Economics, Baghdad University, Iraq, 2002.

  Citation
  Arun Kumar Rao, Himanshu Pandey, "Bayesian Estimation for the Parameter of Gamma Lomax Distribution under Different Loss Functions," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.8, Issue.1, pp.47-51, 2021

• Open Access   Article

  Performance of Logistic Regression and Multilayer Perceptron Neural Network in Classification of Objects

  E. Fajariyanto, M. Fajar

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.8 , Issue.1 , pp.52-55, Feb-2021

  Abstract

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  Abstract
  Employment is a problem that always gets serious attention from the government. Problems with the quality of human resources, motivation, and work culture are influencing factors in labor force problems. Classification becomes important as a means of evaluation and concluding labor force problems. The classification method itself consists of the conventional method (require assumptions) and the robust method (does not require assumptions. This study uses several classification methods including the logistic regression, the backpropagation algorithm, and the backpropagation algorithm with the addition of momentum. The results of this study show that based on simulation, the backpropagation algorithm and the backpropagation with the addition of momentum results in a higher classification accuracy than the logistic regression, the existence of momentum can increase the accuracy of the backpropagation classification with the addition of momentum.

  Key-Words / Index Term
  Performance, Logistic Regression, Neural Network, Classification

  References
  [1] A.T.W. Utami, B.S.S. Ulama, “Penerapan Backpropagation untuk meningkatkan Efektivitas Waktu dan Akurasi pada Data Wall-Following Robot Navigation,” Jurnal Sains dan Seni ITS 4(2), pp. 2337-3520, 2013.
  [2] Badan Pusat Statistik, “Pedoman Pencacah Survei Angkatan Kerja Nasional (SAKERNAS) Semesteran 2015,” Badan Pusat Statistik, Jakarta, 2015.
  [3] Badan Penelitian, Pengembangan dan Informasi Kementerian Tenaga Kerja dan Transmigrasi, “Naskah Akademik Arah Kebijakan Ketenagakerjaan 2014 – 2019,” Badan Penelitian, Pengembangan dan Informasi Kementerian Tenaga Kerja dan Transmigrasi, Jakarta, 2013.
  [4] B. Setyawam, "Pemodelan Regresi Logistik pada Kasus Berat Badan Lahir Rendah (BBLR) dan Pengaruh Agregasi Data terhadap Hasil Pendugaan ", Institut Pertanian Bogor, Bogor, 2015.
  [5] D. Puspitaningrum, “Pengantar Jaringan Saraf Tiruan,” Penerbit ANDI, Yogyakarta, pp. 129-133, 2006.
  [6] D. W. Hosmer, S. Lemeshow, ”Applied Logistic Regression,” John Wiley and Sons, Canada, pp. 6-14, 2000.
  [7] E. Naibaho, “Perbandingan Backpropagation Neural Network dan Learning Vector Quantization (Studi Kasus: Klasifikasi Daerah Tertinggal di Indonesia Tahun 2015).” Padjadjaran University, Bandung, 2015.
  [8] F. A. Hermawanti, “Data Mining,” Penerbit ANDI, Yogyakarta, 2007.
  [9] H.J. Song, S.K. Ko, J.D. Kim, C.Y. Park, “Looking for the optimal machine learning algorithm for the Ovarian cancer screening,” International Journal of Bio-Science and Bio-Technology 5(2), pp. 41-48, 2013.
  [10] J.D. Paola, R.A. Schowengerdt, “A Review and Analysis of Backpropagation Neural Networks for Classification of Remotely Sensed Multi-Spectral Imagery,” International Journal of Remote Sensing 16(16), pp. 3033-3058, 1995.
  [11] M. Mentari, “Klasifikasi Menggunakan Kombinasi Multilayer Perceptron dan Alignment Particle Swarm Optimization,” Seminar Nasional Teknologi Informasi dan Komputasi, 2014.
  [12] M. Maulidya, “Perbandingan Analisis Diskriminan dan Regresi Logistik (Studi Kasus Klasifikasi Konsumen Berdasarkan Tempat Berbelanja di Wilayah Taman-Sidoarjo),” Math UNESA 3(1), 2014.
  [13] M. Sinungan,” Produktivitas: Apa dan Bagaimana,” Penerbit Bumi Aksara, Jakarta, 2005.
  [14] M. Zare, H.R. Pourghasemi, M. Vafakhah, B. Pradhan, “Landslide Susceptibility Mapping at Vaz Watershed (Iran) Using an Artificial Neural Network Model: a Comparison Between Multilayer Perceptron (MLP) and Radial Basic Function (RBF) Algorithms,” Arab Journal Geosciences 6 (8), pp. 1-16, 2012.
  [15] N. Kamiyama, N. Iijima, A. Taguchi, H. Mitsui, Y. Yoshida, M. Sone, “Tuning of Learning Rate and Momentum on Back-Propagation,” Singapore ICCS/ISITA, pp. 528-532, 1992.
  [16] P.L. Liew, Y.C. Lee, Y.C. Lin, T.S. Lee, W.J Lee, W. Wang, C.W. Chien, “Comparison of Artificial Neural Networks with Logistic Regression in Prediction of Gallbladder Disease Among Obese Patients,” Digestive and Liver Disease 39, pp. 356-362, 2007.
  [17] R. Sastri, "Pemodelan Kejadian Kematian Bayi di Indonesia menggunakan Regresi Logistik Terboboti ", Institut Pertanian Bogor, Bogor, 2015.
  [18] S. Mishra, V.K. Singh, ”Monthly Energy Consumption Forecasting Based On Windowed Momentum Neural Network. IFAC-PapersOnLine 48-30, pp. 433–438, 2015.

  Citation
  E. Fajariyanto, M. Fajar, "Performance of Logistic Regression and Multilayer Perceptron Neural Network in Classification of Objects," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.8, Issue.1, pp.52-55, 2021

• Open Access   Article

  Comparison between Markov chain Techniques for Future Forecasting Using Production and Consumption of Electric Energy

  Mohammed Alqatqat, Ma Tie Feng

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.8 , Issue.1 , pp.56-71, Feb-2021

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  Abstract
  Forecasting of electric energy production and consumption is a fundamental phenomenon for a country’s planning. This study aims to make a comparison between four forecasting methods namely Markov developed by Fiering and Thomas , Markov chains Transformation , Fuzzy Time Series Markov Chain , and proposed method By combining two methods Markov developed by Fiering and Thomas and Markov chains Transformation of predict future production and consumption of electricity in 2016, 2017, 2018,2019 this study examined the accuracy of the prediction of production and consumption of electric energy based on monthly data for production and consumption of electricity during 2016 to 2019 in China. By comparing the four models using the Mean Absolute Percentage Error (MAPE), proposed method was found as the most appropriate model for predicting and analysing the data of interest. Based on this model, production and consumption of electricity can be forecasted. The predictive values were consistent with the original values of the series, which indicated the efficiency of the proposed method in production electric with lowest MAPE but the predictive of consumption electric get low MAPE by Markov chains Transformation and we note that the random number in Markov developed by Fiering and Thomas has impact in our proposed method.

  Key-Words / Index Term
  Time series analysis, predictive modelling, Markov chains, Fuzzy time series

  References
  [1] Weron R. “Electricity price forecasting: A review of the state-of-the-art with a look into the future”, International Journal of Forecasting.Vol.30, Issue.4, pp.1030-1081, 2014
  [2] Bianco V, Manca O, Nardini S, et al. “Analysis and forecasting of nonresidential electricity consumption in Romania”, Applied Energy Journal. Vol.87, Issue.11, pp.3584-3590, 2010
  [3] An N, Zhao W, Wang J, et al. ” Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting”. Energy Journal. Vol.49, pp.279-288, 2013
  [4] Zhao W, Wang J, Lu H. ”Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model”, Omega. Vol.45, pp.80-91, 2014
  [5] Göb R, Lurz K, Pievatolo A. ” Electrical load forecasting by exponential smoothing with covariates”. Applied Stochastic Models in Business and Industry.Vol.29, Issue 6, pp.629-645, 2013
  [6] Taylor JW. ”Short-term electricity demand forecasting using double seasonal exponential smoothing”. Journal of the Operational Research Society. Vol.54, Issue.8, pp.799-805, 2017
  [7] Rahman A, Ahmar AS. Forecasting of primary energy consumption data in the United States: A comparison between ARIMA and Holter-Winters models. 2017;1885:020163. Vol.87, Issue.11, pp.3584-3590, 2010
  [8] Kristie Seymore AM, ”Ronald Rosenfeld. Learning Hidden Markov Model Structure for Information Extraction”. AAAI Technical Report WS. Vol.99, Issue.11, pp.37-42, 1999
  [9] Zhou P, Ang B, Poh K. ”A trigonometric grey prediction approach to forecasting electricity demand”. Energy. Vol.31, Issue.14, pp.2839-2847, 2006
  [10] Azadeh A, Saberi M, Seraj O. ”An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran”. Energy.Vol.35, Issue.6, pp.2351-2366, 2010
  [11] Taylor J W, Majithia S. ”Using combined forecasts with changing weights for electricity demand profiling”. Journal of the Operational Research Society Vol.51, Issue.1, pp.72-82, 2000
  [12] Castelnuovo, Efrem, 2003. "Taylor rules, omitted variables, and interest rate smoothing in the US," Economics Letters, Elsevier, Vol.81, , Issue.1, pp.55-59, 2003
  [13] Taylor J W, Majithia S. "Using combined forecasts with changing weights for electricity demand profiling [J] ". Journal of the Operational Research Society, Vol.51, pp.72-82, 2000
  [14] Gupta RS. “Hydrology and hydraulic systems”. Waveland Press, USA, pp. 242-245, 2016.
  [15] Guerrero VM. “Time?series analysis supported by power transformations”. Journal of Forecasting. Vol.12, Issue.1,pp.37-48, 1993
  [16] Fiering MB, Bund B, Jackson BB. “Synthetic stream flows”. Vol. 1. American Geophysical Union; 1971. Vol.32, Issue.1, pp.73-76, 1971
  [17] Mark A. Pinsky, Samuel Karlin, in “An Introduction to Stochastic Modeling” (Fourth Edition), USA, pp. 87-102, 2011.
  [18] Stephen And rilli, David Hecker, “ in Elementary Linear Algebra (Fourth Edition) ”, USA, pp. 318-322, 2010.
  [19] W.J. Stewart, “Introduction to Numerical Solutions of Markov Chains”, Princeton University Press, Princeton, USA, pp. 65-68, 1994.
  [20] Tsaur, R. C. A” Fuzzy Time Series- Markov Chain Model with an Application to Forecast the Exchange Rate between the Taiwan and US Dollar”. International journal of innovative computing information and control, Vol.8, Issue.7B, pp.4931-4942, 2010
  [21] Xihao, S. dan Yimin, L. 2008. ” Average-Based Fuzzy Time Series Models for Forecasting Shanghai Compound Index”. World journal of modelling and simulation. Vol.4, , Issue 2 ,pp.104-111, 2008
  [22] Yaffee RA, McGee M.” An introduction to time series analysis and forecasting: with applications of SAS”® and SPSS®. Elsevier; Netherlands, pp. 173-182, 2000.
  [23] Taylor, J.W. Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, Vol .54, pp.799-805, 2003
  [24] Akgün et al., Organizational learning: A socio-cognitive framework. Human Relations, Vol 56, pp. 839-868, 2003
  [25] Montgomery, D. C., Jennings, C. L., Kulahci, M. Introduction to Time Series Analysis and Forecasting. John Wiley & Sons, Inc. USA, pp.212-236, 2008.
  [26] Box, G. E. P., & Jenkins, G. M.. Time series analysis: Forecasting and control. San Francisco: Holden-Day. USA, pp.341-348, 1976
  [27] Brown, R. G. Smoothing, Forecasting and Prediction of Discrete Time Series. Prentice Hall, Englewood Cliffs, N. J. USA, pp.78-85, 1962.

  Citation
  Mohammed Alqatqat, Ma Tie Feng, "Comparison between Markov chain Techniques for Future Forecasting Using Production and Consumption of Electric Energy," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.8, Issue.1, pp.56-71, 2021

• Open Access   Article

  A Generalization to Integral e^-x. (f(x)-f’(x))dx

  Toyesh Prakash Sharma

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.8 , Issue.1 , pp.72-75, Feb-2021

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  Abstract
  with the help of this paper, the author is providing a generalization related to the integral of for bringing the main result author used concept of integral by parts, the concept of mathematical induction, integration, differentiation etc. by applying author’s generalization we can easily solve many time-consuming integrals which may a good thing for solvers to find the given integrals in less duration of time respectively. Most of the times peoples can’t think about founding generalizations of those problems that they solved. Those who tries, loose their motive due to complexities in calculations and that’s why only some of them found generalization to a specific expression or problem respectively.

  Key-Words / Index Term
  Induction, Integral by Parts, Integral, derivative, Function etc.

  References
  [1] Toyesh Prakash Sharma, "A Generalization to ? e x (ƒ (x) + ƒ1(x))dx," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.7, Issue.6, pp.46-50, 2020.
  [2] NCERT- Mathematics Textbook for class XI Ch-4 Principles of Mathematical induction. P.86 .
  [3] NCERT- Mathematics Textbook for class XII Ch-7 integrals Ex-7.6. P.323-328. ISBN-81-7450-653-5.
  [4] Joseph Edwards: Integral Calculus for beginners, Ch-IV “ Integral by Parts.. Publisher-arihant P 32 ISBN-978-93-5094-145-4.
  [5] R.D Sharma “Mathematics class-XII” Vol. 1 Ch- 19 Indefinate integral Ex-19.26, publication Danpat Pai Publications rivised edition 2020 ISBN-978-81-941926-5-7. p.19.137-19.151
  [6] NCERT- Mathematics Textbook for class XII Ch-7 integrals Example 21 before Ex-7.6. P.326. ISBN-81-7450-653-5

  Citation
  Toyesh Prakash Sharma, "A Generalization to Integral e^-x. (f(x)-f’(x))dx," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.8, Issue.1, pp.72-75, 2021

• Open Access   Article

  Improving the Reliability of System by Standby Redundancy Method

  Maryam Mohiuddin, R. Kannan, Shabir A. Dar

  Research Paper | Journal-Paper (IJSRMSS)

  Vol.8 , Issue.1 , pp.76-82, Feb-2021

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  Abstract
  Safety is main concern before designing the system. Most of the theories’ are applied for improving the safety of the system, but the reliability theory plays a important role for designing the system as such, that we reach the higher levels of safety.In this paper, a Series- Parallel system is considered for reliability improvement. The said system is a combination of three items in which, two items are connected in parallel and third item is connected in series. It is assumed that the items are independent and follows alpha power Rama distribution. Two methods are applied to this system in order to enhance its reliability. In each method, different set of items are considered for improvement. A data analysis is performed, to compare different improvement methods. At last we find that among these methods, reduction method is proved to be best method for improving the system reliability.

  Key-Words / Index Term
  Reliability Analysis, Reduction Method, Hot Duplication Method.

  References
  [1] L. Rade, “Reliability equivalence, studies in statistical quality control andreliability”,Mathematical Statistics, Chalmers University of Technology, 1989.
  [2] L.Rade, “Reliability Systems of 3-state components, studies in statistical quality control and reliability”, Mathematical Statistics. Chalmers University of Technology, 1990.
  [3] L.Rade, “Performance measures for reliability with a cold standby with random switch, studies in statistical quality control and reliability”. Mathematical Statistics, Chalmers University of Technology, 1991.
  [4] L. Rade, “Reliability equivalence”. Microelectronics and Reliability,33, 881-194, 1993.
  [5] A. M. Sarhan, “Reliability Equivalence with a Basic Series/Parallel System”, Applied Mathematics and Computation, Vol. 132, pp. 115-133, 2002.
  [6] A. M. Sarhan, J. Apaloo, “Exponentiated Modified Weibull Extension Distribution”, Reliability Engineering and System Safety, Vol. 112, pp. 137-144, 2013.
  [7] A. M. Sarhan, A. E. A. Ahmad et.al, “Exponentiated Generalized Linear Exponential Distribution”, Applied Mathematical Modelling, Vol. 37, pp. 2838-2849, 2013.
  [8] A. M. Sarhan, “Reliability equivalence factors of a bridge network system”,International Journal of Reliability and Applications, 2, 81-103, 2002.
  [9] A. M. Sarhan, “Reliability Equivalence of Independent and non identical components series system”, Reliability Engineering & System Safety,67 , 293-300, 2000.
  [10] Abdelfattah Mustafa, “improving the reliability of a series-parallel system using modified Weibull distribution”, International Mathematical Forum, 12, 257-269, 2017.
  [11] A. Mustafa, A. A. El-Faheem, “Reliability Equivalence Factors of a System with Mixture of n Independent and Non-identical Lifetimes with Delay Time”, Journal of Egyptian Mathematical Society, Vol. 22 , pp. 96-101, 2013.
  [12] A. Mustafa, “Reliability Equivalence Factors for Some Systems with Mixture Weibull Failure Rates”, American Journal of Mathematics and Statistics, Vol. 2, No. 1, pp. 6-12, 2012.
  [13] M. Hatim, A. H. Mohammad, “Reliability Performance of Improved General Series-Parallel Systems in the generalized Exponential Lifetime Model”, International Journal of Performability Engineering, 15(6), 1734-1743, 2019.
  [14] A. Yousry, M.L. Al-Ohally, “Reliability Equivalence Factors in Exponentiated Distribution”, Wulfenia, 20(3), 75-85, 2013.

  Citation
  Maryam Mohiuddin, R. Kannan, Shabir A. Dar, "Improving the Reliability of System by Standby Redundancy Method," International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.8, Issue.1, pp.76-82, 2021