Please use this identifier to cite or link to this item:
Estimation of Reference Evapotranspiration using Climatic Data.
|Title:||Estimation of Reference Evapotranspiration using Climatic Data.|
|Authors:||Lum, Margaret K.|
|Contributors:||Civil Engineering (department)|
|Date Issued:||May 2017|
|Publisher:||University of Hawaiʻi at Mānoa|
|Abstract:||In this study, daily reference evapotranspiration (ET0) was estimated from climatic data|
using Multivariate Adaptive Regression Splines (MARS), M5 Model Tree (M5MT), and Gene
Expression Programming (GEP). These approaches were trained with climatic data from eight
weather stations in Iran for years 2000-2007. Thereafter, they were tested with data from the
same eight weather stations in Iran for year 2008 and fourteen weather stations in California for
year 2015. Four data combinations were evaluated: daily mean air temperature, daily mean wind
speed, daily mean relative humidity, and solar radiation (configuration 1); daily mean air
temperature and solar radiation (configuration 2); daily mean air temperature and daily mean
relative humidity (configuration 3); daily maximum, minimum, and mean air temperature, and
extraterrestrial radiation (configuration 4). In the first part of the study, MARS, M5MT, and GEP
models were tested with data from the same Iran stations they were trained with. In the second
part of the study, these approaches were tested with data from stations in California. The
performance of MARS, M5MT, and GEP models were evaluated using mean absolute error
(MAE), root mean square error (RMSE), and the coefficient of determination (R2). For all
approaches, configuration 1 produced the most accurate results. Configuration 4 was found to be
region dependent and is suggested when region specific data is limited (i.e., only temperature
data is available). Results indicated MARS, M5MT and GEP could successfully predict ET0
from climatic data. Comparison of these approaches showed that GEP yielded the most accurate
results. Also, it was found that MARS outperformed M5MT.
|Description:||M.S. Thesis. University of Hawaiʻi at Mānoa 2017.|
|Rights:||All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.|
|Appears in Collections:||
M.S. - Civil Engineering|
Please email firstname.lastname@example.org if you need this content in ADA-compliant format.
Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.