Analisis evapotranspirasi potensial pada berbagai model empiris dan jaringan syaraf tiruan dengan data cuaca terbatas
DOI:
https://doi.org/10.31028/ji.v15.i2.71-84Kata Kunci:
evapotranspirasi potensial, jaringan syaraf tiruan, kebutuhan air tanaman, model empiris, parameter cuacaAbstrak
Data cuaca sangat diperlukan dalam penentuan kebutuhan air tanaman, namun sering kali ketersediaan stasiun cuaca di lapangan masih terbatas. Untuk itu, perlu dilakukan analisis berbagai model evapotranspirasi potensial (ETp) dengan beragam paramater input, termasuk juga model Jaringan Syaraf Tiruan (JST) sebagai pertimbangan dalam penentuan kebutuhan air tanaman. Tujuan makalah ini adalah 1) mengembangkan model JST untuk menduga ETp, 2) membandingkan berbagai model ETp termasuk model JST dengan model standar FAO, 3) untuk menganalisis kebutuhan air tanaman dengan model tersebut, dan 4) menentukan parameter input yang direkomendasikan untuk pendugaan ETp. Analisis didasarkan pada data pengukuran parameter cuaca pada dua musim tanam padi, yaitu pada bulan April - Agustus 2017 dan Januari - Mei 2018. Terdapat 8 Model ETp (model empiris) dan 3 model JST dengan kombinasi parameter input. Hasil penelitian ini menunjukkan bahwa Model JST-2 dengan dengan parameter input radiasi matahari merupakan model JST terbaik dengan nilai R2 0,91-0,92 dan RMSE 0,284 mm dan 0,287 mm untuk percobaan tahun 2017 dan 2018. Model ETp Turc, salah satu model ETp empiris dengan parameter input suhu udara dan radiasi matahari, merupakan model terbaik dengan nilai R2 tertinggi dan RMSE terendah. Sehingga kedua model tersebut merupakan model terbaik dengan nilai ETp total yang mendekati ETp standard FAO. Selain itu, parameter suhu udara dan radiasi matahari merupakan parameter yang direkomendasikan untuk diukur dalam penentuan kebutuhan air tanaman menggunakan model ETp Turc. Tetapi apabila hanya satu parameter yang dapat diukur, maka direkomendasikan untuk mengukur radiasi matahari dengan model JST-2 untuk penentuan evapotranspirasi potensial.
Unduhan
Referensi
Ali, Md. H., & Shui, L. T. (2008). Potential evapotranspiration model for Muda Irrigation Project, Malaysia. Water Resources Management, 23(1), 57. https://doi.org/10.1007/s11269-008-9264-6
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop Evapotranspiration Guidelines for Computing Crop Water Requirements (FAO Irrigation and Drainage Paper No. 56). Rome, Italy: Food and Agriculture Organization.
Arif, C., Setiawan, B. I., Mizoguchi, M., & Doi, R. (2012). Estimation of soil moisture in paddy field using Artificial Neural Networks. International Journal of Advanced Research in Artificial Intelligence (IJARAI), 1(1), 17-21.
Arif, C., Setiawan, B. I., Widodo, S., Hasanah, N. A. I., Mizoguchi, M., & others. (2015). Pengembangan model jaringan saraf tiruan untuk menduga emisi gas rumah kaca dari lahan sawah dengan berbagai rejim air. Jurnal Irigasi, 10(1), 1-10.
Bogawski, P., & Bednorz, E. (2014). Comparison and validation of selected evapotranspiration models for conditions in Poland (Central Europe). Water Resources Management, 28(14), 5021-5038. https://doi.org/10.1007/s11269-014-0787-8
Brouwer, C., & Heibloem, M. (1986). Irrigation water management: Irrigation water needs (Irrigation Water Management Training Manual No. 3). Rome, Italy: Food and Agriculture Organization. Diperoleh dari http://www.fao.org/docrep/S2022E/S2022E00.htm
Choi, E. C. C. (1994). Parameters affecting the intensity of wind-driven rain on the front face of a building. Journal of Wind Engineering and Industrial Aerodynamics, 53(1), 1-17. https://doi.org/10.1016/0167-6105(94)90015-9
Doorenbos, J., & Pruitt, W. O. (1977). Guidelines for Predicting Crop Water Requirements (FAO Irrigation and Drainage Paper No. 24). Rome, Italy: Food and Agriculture Organization.
Douglas, E. M., Jacobs, J. M., Sumner, D. M., & Ray, R. L. (2009). A comparison of models for estimating potential evapotranspiration for Florida land cover types. Journal of Hydrology, 373(3), 366-376. https://doi.org/10.1016/j.jhydrol.2009.04.029
Estévez, J., Gavilán, P., & Berengena, J. (2009). Sensitivity analysis of a Penman-Monteith type equation to estimate reference evapotranspiration in southern Spain. Hydrological Processes, 23(23), 3342-3353. https://doi.org/10.1002/hyp.7439
Falamarzi, Y., Palizdan, N., Huang, Y. F., & Lee, T. S. (2014). Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs). Agricultural Water Management, 140, 26-36. https://doi.org/10.1016/j.agwat.2014.03.014
Fisher, J. B., DeBiase, T. A., Qi, Y., Xu, M., & Goldstein, A. H. (2005). Evapotranspiration models compared on a Sierra Nevada forest ecosystem. Environmental Modelling & Software, 20(6), 783-796. https://doi.org/10.1016/j.envsoft.2004.04.009
Gebler, S., Hendricks Franssen, H.-J., Pütz, T., Post, H., Schmidt, M., & Vereecken, H. (2015). Actual evapotranspiration and precipitation measured by lysimeters: A comparison with eddy covariance and tipping bucket. Hydrology and Earth System Sciences, 19(5), 2145-2161. https://doi.org/10.5194/hess-19-2145-2015
Handoko (Ed.). (1995). Klimatologi Dasar: Landasan Pemahaman Fisika Atmosfer dan Unsur- Unsur Iklim. Jakarta, Indonesia: Pustaka Jaya.
Jensen, M. E. (Ed.). (1980). Design and Operation of Farm Irrigation Systems. Michigan, USA: American Society of Agricultural Engineers.
June, T., Dewi, N. W. S. P., & Meijide, A. (2018). Perbandingan metode aerodinamik, Bowen Ratio dan Penman-Monteith dalam penentuan evapotranspirasi pertanaman kelapa sawit. Agromet, 32(1), 11-20. https://doi.org/10.29244/j.agromet.32.1.11-20
Khoob, A. R. (2008). Comparative study of Hargreavess and artificial neural networks methodologies in estimating reference evapotranspiration in a semiarid environment. Irrigation Science, 26(3), 253-259. https://doi.org/10.1007/s00271-007-0090-z
Kisi, O. (2014). Comparison of different empirical methods for estimating daily reference evapotranspiration in mediterranean climate. Journal of Irrigation and Drainage Engineering, 140(1), 04013002. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000664
Kumar, M., Raghuwanshi, N. S., & Singh, R. (2011). Artificial neural networks approach in evapotranspiration modeling: A review. Irrigation Science, 29(1), 11-25. https://doi.org/10.1007/s00271-010-0230-8
Kusumadewi, S. (2003). Artificial Intelligence (Teknik dan Aplikasinya). Yogyakarta, Indonesia: Graha Ilmu.
Lippman. (1998). Estimating Potential Evaporation and Management 2nd Edition. Virginia, USA: Reston Publishing Company, Inc.
Manik, T. K., Sanjaya, P., & Rosadi, R. A. B. (2017). Comparison of different models in estimating standard evapotranspiration in Lampung Province, Indonesia. International Journal of Environment, Agriculture and Biotechnology, 2(5), 2309-2318.
Maulé, C., Helgalson, W., McGinn, S., & Cutforth, H. (2005). Estimation of standardized reference evapotranspiration on the Canadian Prairies using simple models with limited weather data. Dipresentasikan pada CSAE/SCGR 2005 Meeting, Winnipeg, Canada. Diperoleh dari http://www.csbe-scgab.ca/docs/meetings/2005/05-054.pdf
Mulyadi, M., Soekarno, I., & Winskayati, W. (2014). Analisis pilar modernisasi irigasi dengan pendekatan Analytical Hierarchy Process (AHP) pada Daerah Irigasi Barugbug-Jawa Barat. Jurnal Teknik Sipil, 21(3), 213-220.
Perugu, M., Singam, A. J., & Kamasani, C. S. R. (2013). Multiple Linear Correlation Analysis of Daily Reference Evapotranspiration. Water Resources Management, 27(5), 1489-1500. https://doi.org/10.1007/s11269-012-0250-7
Qian, Y., Wang, W., Leung, L. R., & Kaiser, D. P. (2007). Variability of solar radiation under cloud-free skies in China: The role of aerosols. Geophysical Research Letters, 34(12). https://doi.org/10.1029/2006GL028800
Setiawan, B. I., Imansyah, A., Arif, C., Watanabe, T., Mizoguchi, M., & Kato, H. (2014). SRI paddy growth and GHG emissions at various groundwater levels. Irrigation and Drainage, 63(5), 612-620.
Sharifi, A., & Dinpashoh, Y. (2014). Sensitivity Analysis of the Penman-Monteith reference Crop Evapotranspiration to Climatic Variables in Iran. Water Resources Management, 28(15), 5465-5476. https://doi.org/10.1007/s11269-014-0813-x
Tukimat, N. N. A., Harun, S., & Shahid, S. (2012). Comparison of different methods in estimating potential evapotranspiration at Muda Irrigation Scheme of Malaysia. Journal of Agriculture and Rural Development in the Tropics and Subtropics (JARTS), 113(1), 77-85.
Wati, T., Pawitan, H., & Sopaheluwakan, A. (2015). Pengaruh parameter cuaca terhadap proses evaporasi pada interval waktu yang berbeda. Jurnal Meteorologi dan Geofisika, 16(3), 155-165. https://doi.org/10.31172/jmg.v16i3.286
Wu, I. P. (1997). A simple evapotranspiration model for Hawaii: The Hargreaves model (CTAHR Fact Sheet, Engeineers Notebook No. 106). Honolulu, Hawaii: University of Hawaii. Diperoleh dari https://scholarspace.manoa.hawaii.edu/bitstream/10125/12218/EN-106.pdf
Xing, X., Liu, Y., Zhao, W., Kang, D., Yu, M., & Ma, X. (2016). Determination of dominant weather parameters on reference evapotranspiration by path analysis theory. Computers and Electronics in Agriculture, 120, 10-16. https://doi.org/10.1016/j.compag.2015.11.001
Xu, C. Y. (2002). Textbook of Hydrologic Models. Sweden: Uppsala University.
Yassin, M. A., Alazba, A. A., & Mattar, M. A. (2016). Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate. Agricultural Water Management, 163, 110-124. https://doi.org/10.1016/j.agwat.2015.09.009
Zhao, P., Li, S., Li, F., Du, T., Tong, L., & Kang, S. (2015). Comparison of dual crop coefficient method and Shuttleworth-Wallace model in evapotranspiration partitioning in a vineyard of northwest China. Agricultural Water Management, 160, 41-56. https://doi.org/10.1016/j.agwat.2015.06.026
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Naskah dilisensikan berdasarkan Creative Commons Attribution-ShareAlike 4.0 International License.