Weather and Stochastic Forecasting Method for Generated Discharge Level Data at Sathanur Dam
S. Sathish1, SK. Khadar Babu2 and C.U. Tripura Sundari3
1S. Sathish, Research Scholar, Department of Mathematics, VIT, Vellore, (Tamil Nadu), India.
2Dr. SK. Khadar Babu, Assist Prof, Department of Mathematics, VIT, Vellore, (Tamil Nadu), India.
3Dr. C.U. Tripura Sundari, Assist Prof, Department of Statistics, Pondichery University, Pondichery, India.
Manuscript received on 04 August 2019 | Revised Manuscript received on 08 August 2019 | Manuscript published on 30 August 2019 | PP: 3797-3802 | Volume-8 Issue-10, August 2019 | Retrieval Number: J99810881019/2019©BEIESP | DOI: 10.35940/ijitee.J9981.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This article forecasts the future values using stochastic forecasting models for specified fitted values by using downscaling data, which are collected from Sathanoor Dam gauging site. Due to the demand of the water in this current scenario, this study analyzed the perdays Discharge level data collected from Sathanoor Dam where the outcome is predicted in a downscaling data set in hydrology, extended Thomas –Fiering, ARIMA, MLE models, is used to estimate perdays discharge level data of each month. The error estimates RMSE, MAE of forecasts from above models is compared to identify the most suitable approaches for forecasting trend analysis.
Keywords: Stochastic Process, Thomas –Fiering, ARIMA, MLE Models, Forecast.
Scope of the Article: Data Mining