Perilaku Berbagai Data Iklim sebagai Input Tunggal untuk Model JST dari Evapotranspirasi Potensial Harian Penman-Monteith

Danayanti Azmi Dewi Nusantara, Feriza Nadiar, Mohamad Bagus Ansori

Abstract


ABSTRAK

 

Di Indonesia sebagai daerah yang memiliki iklim tropis, menghitung jumlah evapotranspirasi potensial (ETp) harian menjadi penting. Selain itu, ketika pada musim kemarau, laju ETp tumbuh menjadi signifikan untuk memverifikasi keseimbangan air. EP berkembang menjadi sangat penting untuk kasus ketersediaan air seperti irigasi, pasokan air, tenaga air, dll. Model EP dibentuk dari berbagai input data iklim yaitu kecepatan angin, kelembaban relatif, durasi radiasi matahari, suhu rata-rata, penguapan, dan curah hujan. Langkah pemodelannya panjang dan rumit. Penggunaan Jaringan Syaraf Tiruan (JST) sebagai pemodelan berbasis data untuk menyederhanakan proses pemodelan. Model JST dari EP akan ditargetkan untuk mendekati EP yang dihitung dengan Penman-Monteith. Penelitian ini bertujuan untuk mengetahui perilaku masing-masing data iklim sebagai input tunggal untuk model JST dari evapotranspirasi potensial. Nilai MSE dan nilai R pada proses validasi JST dapat menunjukkan bagaimana perbedaan antara hasil data iklim tertentu dengan set data iklim lengkap. Hasil dari penelitian ini adalah kelembapan relatif menyajikan model JST terbaik dengan input data iklim tunggal daripada yang lain. Selain itu, menunjukkan bahwa kelembapan relatif sebagai input signifikan ke model PET menggunakan JST maupun tidak.

Kata kunci : evapotranspirasi; JST; penman; iklim

 

ABSTRACT

 

 In Indonesia, as a region that has a tropical climate, calculating the amount of daily potential evapotranspiration (PET) becomes essential. Also, when on the drought season, the rate of PET grows into significant to verify the water balance. The PET develops into crucial for water availability cases such as irrigation, water supply, hydropower, etc. The PET model established from various input of climate data that are wind speed, relative humidity, the duration of solar radiation, average temperature, evaporation, and rainfall. The step of modeling is long and complicated. It is using Artificial Neural Network (ANN) as data-driven modeling to simplifies the process of modeling PET. The ANN PET model will be targeted to approach the PET calculated with Penman-Monteith. This research aimed to know the behavior of each of the climate data as a single input to the ANN PET model. The MSE-value and R-value on the validation process of ANN can show how the differential between the results of particular climate data to the full data set. The outcome of this research is the relative humidity presents the best ANN model with a single input of climate data than others. Besides, it makes the relative humidity as a doubtless significantly input to the PET model even using ANN or not. 



Keywords


evapotranspiration; ANN; penman; climate

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DOI: http://dx.doi.org/10.33366/rekabuana.v4i2.1418

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