Forecasting Discharge Level time Series on Statistical Parametric Approach using Hydrological Data

S. Sathish¹ , S.K. Khadar Babu²

Section:Research Paper, Product Type: Journal-Paper
Vol.6 , Issue.11 , pp.24-28, Nov-2020

Online published on Nov 30, 2020

Copyright © S. Sathish, S.K. Khadar Babu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
At present, water is a critical part for people, animals, autotrophs and heterotrophs. In actuality, people continually grasped to their state of being. Due to the extended people, human requirements more water for drinking, farming, and so on. The proposed paper explained in detail, the standard measures like mean, standard deviation, co-efficient of R2 and auto-correlations of the downscaling data in hydrology. The new model is MLE by Gaussian dispersion is a strategy that we will discover the estimations of that outcome are best fit the informational collections. Straightforward downscaling approach, when all is said in done, can perform well as the parametric technique, produce the noticed water level utilizing SELGA and SELSGA approaches. Maximum Likelihood Estimation is the best forecast utilizing boundaries of water level informational collections. The new model is utilized, the present proposed article is to foresee future qualities utilizing stochastic extended straight gathering normal and stochastic expanded direct semi-bunch normal on produced downscaling informational indexes.

Key-Words / Index Term :
Stochastic process, Seasonal periods, Moving average, stochastic extended linear group average, stochastic extended linear semi-group average, Maximum likelihood estimation

References :
[1] S. Willium, “Classsification of SKC Algo,” IEEE Transaction, Vol.31, Issue.4, pp.123-141, 2012.
[2] A. Adib, A.R.M. Majd, “Optimization of Reservoir Volume by Yield Model And Simulation of it by Dynamic Programming and Markov Chain Method,” American-Eurasion J. Agric. & Environ. Sci, Vol. 5, Issue.6, pp.796-803, 2009.
[3] K. Ayob, S.D. Amat, S. D, “Water Use Trend at Universiti Tekologi Malaysia. Application Of Arima Model,” Jurnal Teknology, Vol. 41 (B), pp. 47-56, 2004.
[4] Andreas Behr, Sebastian Tente, “Stochastic frontier analysis and means of Maximum Likelihood and the method of moments,” pp. 978-3-319-20502-18, 2008.
[5] MV. Ahmadi, M. Doostparast, J. Ahmadi, “Estimating the lifetime performance index with Weibull distribution based on progressive first-failure censoring scheme. Journal of Computational and Applied Mathematics” 23, pp. 93–102MathSciNetCrossRefGoogle Scholar, 2013.
[6] W.R. Bell, “An Introduction to Forecasting with Time Series Models,” Insurance Mathematics and Economics 3, pp.241-255, 1984.
[7] B.L. Bowerman, R.T. O’Connell, “Forecasting and Time Series, An Applied Appr"oach,” Third Edition. Duxbury Press, 1993.
[8] U. Balasooriya, “Failure-censored reliability sampling plans for the exponential distribution,” J Stat Comput Simul, Vol. 52, Issue. 4, pp. 337–349, 1995.
[9] T.W. Hansen, S.J. Mason, L. Sun, A. Tall, “Review at seasonal climate forecasting for agriculture in Sub-Saharan Africa.Exp.Agric,” Vol. 47, Issue. 2, pp. 205, 2011.
[10] C. Lee, C. Ko, “Short-term Load Forecasting Using Lifting Scheme and ARIMA Models. Expert Systems with Applications,” Vol. 38, pp.5902-5911, 2011.
[11] G. Naadimuthu, E.S. Lee, “Stochastic Modelling and Optimization of Water Resources Systems. Mathematical Modelling,” Vol. 3, pp. 117-136, 1982.
[12] S. Sathish, SK. Khadar Babu, “Markovian prediction of future values for food,” grains in the economic survey. IOP Conf. Series. Materials Science and Engineering, pp. 263, (042141), 2017.
[13] S. Sathish, SK. Khadar Babu, “Stochastic time series analysis of hydrology data for water resources,” J.Iop Anal.Appl, pp. 263: 042140, 2017.
[14] PJ. Shellito, EE. Small, A. Colliander, A, “In situ following rainfall events,” Geophys Res Lett 1, pp. 1–8, 2016.
[15] Singh, Omvir, Turkiya and Sushila, “Assessing Potential for Rooftop Rainwater Harvesting, An Option for Sustainable Rural Domestic Water Supply in Arid Region of Haryana. Journal Of Rural Development,” Vol. 36, pp. 49-60, 2017.
[16] M.A. Thyer, G. Kuczera, “Modelling long-term persistence in rainfall time series. Sydney rainfall case study. Hydrology and Water Resources Symposium,” pp.550-555, 1999.
[17] M.A. Thyer, G. Kuczera, “Modeling long-term persistence in hydro climatic time series using a hidden state Markov Model,” Water Resources Research, Vol. 27, pp. 3301-3310, 2000.
[18] P.R. Vittal, V. Thangaraj, V. Muralidhar, “Stochastic models for the amount of overflow In a finite dam with random inputs, random outputs and Exponential release policy, Stochastic analysis and Applications,” Vol. 29, pp. 473-485, 2011.
[19] R.L. Wilby, S.P. Charles, E. Zorita, B. Timbal, P. Whetton, L.O. Mearns, “Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods. IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis,” (TGICA), 2004.
[20] R.L. Wilby, H. Hassan, K. Hanaki, “Statistical downscaling of hydro meteorological variables using general circulation model output,” J. Hydrol, Vol. 205, pp. 1–19, 1998.
[21] F. Wetterhall, H. Winsemius, E. Dutra, M. Werner, E. Paper Berger, “Seasonal predictions of agro-metrological drought indicators for the Limpopo basin. Hydrol,” Earth Syst. Sci, Vol. 19, Issue. 6, pp. 2577-2586, 2015.