Volume 11, Issue 22 (10-2020)                   jwmr 2020, 11(22): 210-219 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

siasar H, honar T. Comparison of Performance of GLM, RF and DL Models in Estimation of Reference Evapotranspiration in Zabol Synoptic Station. jwmr. 2020; 11 (22) :210-219
URL: http://jwmr.sanru.ac.ir/article-1-1000-en.html
payam nour university of zabol
Abstract:   (335 Views)
Evapotranspiration is one of the most important components of the hydrology cycle for planning irrigation systems and assessing the impacts of climate change hydrology and correct determination is important for many studies such as hydrological balance of water, design of irrigation irrigation networks, simulation of crop yields, design, optimization of water resources, nonlinearity, inherent uncertainty, and the need for diverse climatic information in estimating evapotranspiration have been the reasons why researchers have used artificial intelligence-based approaches. In this study, to estimate accurately the daily reference evapotranspiration between 2009-2018 in Zabol city, north of Sistan and Baluchestan province,  first was used a standard FAO-Penman-Montith method and Zabol synoptic station meteorological data- the ETo reference transpiration is calculated and then presented by various scenarios of meteorological parameters including: maximum, minimum and mean temperature, maximum, minimum and mean humidity, precipitation, sunshine, wind speed and evaporation as inputs for deep learning models, Random forest and generalized linear model were attempted on a daily time scale More accurately. In estimating daily evapotranspiration in these models, 25 scenarios were selected from meteorological data combination and FAO-Penman-Monteith method was used to evaluate the models. Among the investigated scenarios, the M5 scenario (maximum, minimum and mean temperature, maximum, minimum and mean humidity, wind speed, pan evaporation) for deep learning model with minimum error (0.517) and highest correlation coefficient (0.517). 0.996 had the best performance among the above models. The deep learning model showed more accuracy and stability than other models. Hence, this study is recommended a deep learning model for estimating reference plant evapotranspiration in Sistan plain.
Full-Text [PDF 1064 kb]   (126 Downloads)    
Type of Study: Applicable | Subject: هواشناسی
Received: 2019/03/3 | Revised: 2021/03/3 | Accepted: 2020/05/26 | Published: 2021/03/3

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2021 CC BY-NC 4.0 | Journal of Watershed Management Research

Designed & Developed by : Yektaweb