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Daily Reference Evapotranspiration Estimation using Linear Regression and ANN Models

Daily Reference Evapotranspiration Estimation using Linear Regression and ANN Models The present study investigates the applicability of linear regression and ANN models for estimating daily reference evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar regions of Andhra Pradesh. The climatic parameters influencing daily ET0 were identified through multiple and partial correlation analysis. The daily temperature, wind velocity, relative humidity and sunshine hours mostly influenced the study area in the daily ET0 estimation. Linear regression models in terms of the climatic parameters influencing the region and, optimal neural network architectures considering these influencing climatic parameters as input parameters were developed. The models’ performance in the estimation of ET0 was evaluated with that estimated by FAO-56 Penman–Montieth method. The regression models showed a satisfactory performance in the daily ET0 estimation for the regions selected for the present study. The optimal ANN (4,4,1) models, however, consistently showed an improved performance over regression models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of The Institution of Engineers (India): Series A Springer Journals

Daily Reference Evapotranspiration Estimation using Linear Regression and ANN Models

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Publisher
Springer Journals
Copyright
Copyright © 2013 by The Institution of Engineers (India)
Subject
Engineering; Civil Engineering
ISSN
2250-2149
eISSN
2250-2157
DOI
10.1007/s40030-013-0030-2
Publisher site
See Article on Publisher Site

Abstract

The present study investigates the applicability of linear regression and ANN models for estimating daily reference evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar regions of Andhra Pradesh. The climatic parameters influencing daily ET0 were identified through multiple and partial correlation analysis. The daily temperature, wind velocity, relative humidity and sunshine hours mostly influenced the study area in the daily ET0 estimation. Linear regression models in terms of the climatic parameters influencing the region and, optimal neural network architectures considering these influencing climatic parameters as input parameters were developed. The models’ performance in the estimation of ET0 was evaluated with that estimated by FAO-56 Penman–Montieth method. The regression models showed a satisfactory performance in the daily ET0 estimation for the regions selected for the present study. The optimal ANN (4,4,1) models, however, consistently showed an improved performance over regression models.

Journal

Journal of The Institution of Engineers (India): Series ASpringer Journals

Published: Jul 6, 2013

References