Modeling of Pan Evaporation Based on the Development of Machine Learning
Methods
- URL: http://arxiv.org/abs/2110.04749v1
- Date: Sun, 10 Oct 2021 10:06:16 GMT
- Title: Modeling of Pan Evaporation Based on the Development of Machine Learning
Methods
- Authors: Mustafa Al-Mukhtar
- Abstract summary: Changes in climatic factors, such as changes in temperature, wind speed, sunshine hours, humidity, and solar radiation can have a significant impact on the evaporation process.
The aim of this study is to investigate the feasibility of several machines learning (ML) models for modeling the monthly pan evaporation estimation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: For effective planning and management of water resources and implementation
of the related strategies, it is important to ensure proper estimation of
evaporation losses, especially in regions that are prone to drought. Changes in
climatic factors, such as changes in temperature, wind speed, sunshine hours,
humidity, and solar radiation can have a significant impact on the evaporation
process. As such, evaporation is a highly non-linear, non-stationary process,
and can be difficult to be modeled based on climatic factors, especially in
different agro-climatic conditions. The aim of this study, therefore, is to
investigate the feasibility of several machines learning (ML) models
(conditional random forest regression, Multivariate Adaptive Regression
Splines, Bagged Multivariate Adaptive Regression Splines, Model Tree M5, K-
nearest neighbor, and the weighted K- nearest neighbor) for modeling the
monthly pan evaporation estimation. This study proposes the development of
newly explored ML models for modeling evaporation losses in three different
locations over the Iraq region based on the available climatic data in such
areas. The evaluation of the performance of the proposed model based on various
evaluation criteria showed the capability of the proposed weighted K- nearest
neighbor model in modeling the monthly evaporation losses in the studies areas
with better accuracy when compared with the other existing models used as a
benchmark in this study.
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