Ascertaining Predictability Cognizance for the Prediction of Reference Evapotranspiration
Saravanan Poomalai1, Sivapragasam Chandrasekaran2
1Saravanan Poomalai, Center for Water Technology, Department of Civil Engineering, Kalasalingam Academy of Research and Education, Srivilliputtur (Tamil Nadu), India.
2Sivapragasam Chandrasekaran, Center for Water Technology, Department of Civil Engineering, Kalasalingam Academy of Research and Education, Srivilliputtur (Tamil Nadu), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 30 December 2019 | PP: 170-173 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A10031291S419/19©BEIESP | DOI: 10.35940/ijeat.A1003.1291S419
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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: Reference evapotranspiration (ET0) is a rudimental variable in the estimation of crop water requirement, and preparation of irrigation schedule. Prediction of ET0 is a necessitous one for estimation of crop water requirement in future time step. In this paper ET0 is predicted using Artificial Neural Network (ANN) by different inputs Like Temperature, Cloud cover, Vapor pressure, Precipitation and its combinations by various models. Before prediction, the predictability of all the input time series is calculated individually and the effect of predictability on prediction is analyzed in models having single predictor. In spite of inserting additional predictor in input, the reason for increase of Root mean squared error is justified in terms of predictability in the models having multiple predictors. Also it is seen that the performance of models with multiple predictors is better when compared to single predictor models in the estimation of ET0.
Keywords: Artificial Neural Network, Predictability, Hurst Exponent, Reference Evapotranspiration.
Scope of the Article: Health Monitoring and Life Prediction of Structures