Using ANN to Evaluate the Climate Data that High Affect on Calculate Daily Potential Evapotranspiration with Modified-Penman Method in The Tropical Regions

In Indonesia, as part of tropical regions, calculating the amount of daily potential evapotranspiration (PET) becomes significant to ascertain the water balance. Applied Artificial Neural Network (ANN) as data-driven modeling in the period of Industrial Revolution 4.0 to develop a better world. Usin...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-07, Vol.1569 (4), p.42028
Main Authors: Nusantara, DAD, Nadiar, F
Format: Article
Language:English
Subjects:
Artificial neural networks
Climate
Climate models
Climatology
Evaporation
Evapotranspiration
Humidity
Physics
Radiation
Rainfall
Relative humidity
Solar radiation
Tropical environments
Water balance
Wind speed
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Summary:In Indonesia, as part of tropical regions, calculating the amount of daily potential evapotranspiration (PET) becomes significant to ascertain the water balance. Applied Artificial Neural Network (ANN) as data-driven modeling in the period of Industrial Revolution 4.0 to develop a better world. Using ANN simplifies the process of modeling the various input climate data to count up the amount of daily PET. There six climate data that will be input in the Modified Penman Method, which are average-temperature, wind velocity, rainfall, relative-humidity, evaporation, and the time of solar radiation. Therefore, for further modeling using fewer input data than typically used in this method, so the high affect climatology data must be known. The methodology is to compare the best model, which has input six of the climate data with the model developed by just five of the climate data. The results of this research are the sequence of climate data that influence the daily PET model from the highest to the smallest: wind velocity, relative humidity, the time of solar radiation, temperature, rainfall, evaporation.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1569/4/042028