<p>I find many researchers use Machine Learning Techniques and Algorithms to calculate the EvapoTranspiration, while there’s also the Penmann Monteith equation that gives us the result. Does this say we don’t need Penmann equation anymore?</p>

<p>Here is a research samples:</p>

<p><a href="https://lahore.comsats.edu.pk/Papers/Abstracts/1210-8586787794296626450.pdf" rel="nofollow noreferrer">https://lahore.comsats.edu.pk/Papers/Abstracts/1210-8586787794296626450.pdf</a></p>

<p><a href="https://www.tandfonline.com/doi/pdf/10.1623/hysj.54.5.918" rel="nofollow noreferrer">https://www.tandfonline.com/doi/pdf/10.1623/hysj.54.5.918</a></p>

<p>Thanks</p>

CC BY-SA 4.0
2019-07-23T16:04:10.903Z
Can you give examples of papers that have used machine learning techniques in this way? My hypothesis is that it is due to the ground heat flux, which is hard to measure.
BarocliniCplusplus
CC BY-SA 4.0
2019-07-24T07:19:28.053Z
Hi, I just edited my question and added a link to a paper. Thanks.
Tessnim
CC BY-SA 4.0
2019-08-02T16:52:35.487Z
I personally think, estimating only etp from ML may not be a very innovative research idea but as evapotranspiration plays a major role in water balance and water balance derives all major hydrological processes especially at catchment scale, combining/including evapotranspiration in a machine learning watershed scale hydrological model may prove useful. For example it can be used to maintain water balance in a machine learning based model.
Ather Cheema
CC BY-SA 4.0
2021-08-07T15:26:26.443Z
Assuming that both Penmann equation that you are referring to and the ML models estimate the same variable (e.g. actual ETP) then there could be two factors rendering one more useful that the other: the input variables they use, and the estimation accuracy they provide. The Ideal scenario is to have a model that can estimate ETP with readily available input variables (features) and prove that it has a "good" estimation accuracy. I think we need an honest/objective and comprehensive evaluation of their performances on large scale to end up with a vote.
Alireza Amani
CC BY-SA 4.0
2021-08-07T15:30:50.983Z
One thing that I can do wrong as a researcher is to compare (test) the accuracy of the empirical formulas with a ML model on a location/site that the ML model was trained on. Then my results (probably) would show a huge advantage in favor of the ML model.
Alireza Amani
<evaporation><predictability><research><machine-learning>
Why estimate evapotranspiration using machine learning techniques while we can calculate it with Penmann Monteith equation?
Tessnim
# Why estimate evapotranspiration using machine learning techniques while we can calculate it with Penmann Monteith equation?

(Asked by: username_0 on 2019-07-23)

I find many researchers use Machine Learning Techniques and Algorithms to calculate the EvapoTranspiration, while there’s also the Penmann Monteith equation that gives us the result. Does this say we don’t need Penmann equation anymore?

Here is a research samples:

<https://lahore.comsats.edu.pk/Papers/Abstracts/1210-8586787794296626450.pdf>

<https://www.tandfonline.com/doi/pdf/10.1623/hysj.54.5.918>

Thanks

**Tags:** evaporation, predictability, research, machine-learning

> **Comments:**
> **username_1**: Can you give examples of papers that have used machine learning techniques in this way? My hypothesis is that it is due to the ground heat flux, which is hard to measure.  
> **username_0**: Hi, I just edited my question and added a link to a paper. Thanks.  
> **username_2**: I personally think, estimating only etp from ML may not be a very innovative research idea but as evapotranspiration plays a major role in water balance and water balance derives all major hydrological processes especially at catchment scale, combining/including evapotranspiration in a machine learning watershed scale hydrological model may prove useful. For example it can be used to maintain water balance in a machine learning based model.  
> **username_3**: Assuming that both Penmann equation that you are referring to and the ML models estimate the same variable (e.g. actual ETP) then there could be two factors rendering one more useful that the other: the input variables they use, and the estimation accuracy they provide. The Ideal scenario is to have a model that can estimate ETP with readily available input variables (features) and prove that it has a "good" estimation accuracy. I think we need an honest/objective and comprehensive evaluation of their performances on large scale to end up with a vote.  
> **username_3**: One thing that I can do wrong as a researcher is to compare (test) the accuracy of the empirical formulas with a ML model on a location/site that the ML model was trained on. Then my results (probably) would show a huge advantage in favor of the ML model.  

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## 0 answers

