Modeling of Monthly Pan Evaporation Using M5P Machin Learning Technique
Abstract
The purpose of this study is to investigate the ability of M5P model trees machine learning technique for estimating monthly pan evaporation from meteorological data. The M5 method as it is implemented in the WEKA system is used to generate trees models. Three different M5P models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) are developed to evaluate effect of each of these variables on evaporation estimations. Two error statistics namely root mean squared error and coefficient of determination are used to measure the performance of the developed models. Monthly meteorological data of Emara station in Missan, south of Iraq is used in this study as a case study. The results demonstrated that the M5P models whose inputs are wind speed, relative humidity and temperature performed the best among the input combination tried in the study. It was found that M5P could be employed successfully in modeling evaporation process from the available climatic data.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 Mohamod Abdul Hassan Joehl AL – Janabi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Journal of Kufa-Physics is licensed under the Creative Commons Attribution 4.0 International License, which allows users to copy, to create extracts, abstracts, and new works from the Article, to alter and revise the Article, and to make commercial use of the Article (including reuse and/or resale of the Article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made and the licensor is not represented as endorsing the use made of the work. The authors hold the copyright for their published work on the JKP website, while KJP is responsible for appreciating citation for their work, which is released under CC-BY-4.0 enabling the unrestricted use, distribution, and reproduction of an article in any medium, provided that the original work is properly cited.