Reference Evapotranspiration Modeling Using Artificial Intelligence-Based Approaches: A Bibliometric Analysis
DOI:
https://doi.org/10.54536/ajaset.v8i1.2394Keywords:
Artificial Intelligence, Bibliometric Analysis, Reference Evapotranspiration, Web of ScienceAbstract
A bibliometric analysis was performed over the period 1992–2023, to pinpoint important trends, emphasis, and geographic distribution of international reference evapotranspiration (ETo) modeling research using intelligence-based approaches. Data was mined from the databases of the online version of Web of Science. The data was analyzed using the Excel program, and the bibliometric mapping was performed using the VOSviewer software. A total of 1148 articles met the required criteria. The findings indicated that the number of articles had increased rapidly over the past five years and that English was the prevalent language (99.5%). Researchers in 99 countries have published in this field of research. China ranked first with 356 articles (31.0%), followed by the United States of America with 193 articles (16.8%). Egypt and Saudi Arabia are two of the top 15 countries in the world. These articles were published in 289 journals; the Journal of Hydrology was the most productive journal (95 articles, 8.3%), followed by Computers and Electronics in Agriculture (67, 5.8%). The most productive author is Kisi Ozgur from Turkey (68 articles, 4.2%). Taking into account all the institutions working on ETo modeling (1555 institutions), the Chinese Academy of Science was ranked first (80 articles, 7.0%), followed by the Egyptian Knowledge Bank (55 articles, 4.8%). Artificial neural networks and machine learning were the most commonly used intelligence-based approaches for ETo modeling. Reinforcement learning was the least popular technique for ETo modeling, which could be due to its complexity and data requirements.
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Allen, R. G., Jensen, M. E., Wright, J., & Burman, R. D. (1989). Operational estimates of reference evapotranspiration. Journal of Agronomy, 81(4), 650–662. https://doi.org/10.2134/agronj1989.00021962008100040019x
Arfaoui, A., Ibrahimi, K., & Trabelsi, F. (2019). Biochar application to soil under arid conditions: a bibliometric study of research status and trends. Arabian Journal of Geosciences 12(2), 1–9. https://doi.org/10.1007/s12517-018-4166-2
Benzarti, J., & Riou, C. (1982). Etude expérimentale de l'évapotranspiration potentielle sous serre en climat semi-aride. Annales Institut National Recherche Agronomique Tunis 55(1), 24.
Cavero, J., Farre, I., Debaeke, P., & Faci, J. M. (2000). Simulation of maize yield under water stress with the EPICphase and CROPWAT models. Journal of Agronomy 92(4), 679-690. https://doi.org/10.2134/agronj2000.924679x
Hargreaves, G. H., & Samani, Z. A. (1985). Reference crop evapotranspiration from temperature. Transactions of the American Society of Agricultural and Biological Engineers 1, 96–99. https://doi.org/10.13031/2013.26773
Huo, Z., Feng, S., Kang, S., & Dai, X. (2012). Artificial neural network models for reference evapotranspiration in an arid area of northwest China. Journal of Arid Environments 82, 81-90. https://doi.org/10.1016/j.jaridenv.2012.01.016
Ines, R. L., Daping, P. G. B., Duque, Z. M., & Regalario, R. P. (2023). Estimating Evapotranspiration for Irrigation Scheduling by Using Atmometers. American Journal of Agricultural Science, Engineering, and Technology, 7(2), 30–36. https://doi.org/10.54536/ajaset.v7i2.1430
Jensen, M. E., Burman, R. D., & Allen, R. G. (1990). Evapotranspiration and irrigation water requirements. ASCE Manuals and Reports on Engineering Practices 70, ASCE, New York, p. 360.
Kişi, O. (2006). Generalized regression neural networks for evapotranspiration modeling. Journal of Hydrological Science 51, 1092–1105. ttps://doi.org/10.1623/hysj.51.6.1092
Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., & Pruitt, W. O. (2002). Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering 128(4), 224–233. https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224)
Kwakye, J. (2023). Effect of Temperature and Rainfall Variability on Selected Crop Yields in Wenchi Municipality of Ghana. American Journal of Environment and Climate, 2(1), 24–32. https://doi.org/ 10.54536/ajec.v2i1.1328
Liang, B., Lehmann, J., & Solomon, D. (2006). Black carbon increases cation exchange capacity in soils. Soil Science Society of America Journal 70(5), 1719–1730. https://doi.org/10.2136/sssaj2005.0383
Ozkan, C., Kisi, O., & Akay, B. (2011). Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration. Irrigation Science 29, 431–441. https://doi.org/10.1007/s00271-010-0254-0
Priestley, C. H. B., & Taylor, R. J. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review 100, 81–92.
Sawassi, A., & Khadra, R. (2021). Bibliometric network analysis of “Water Systems” adaptation to climate change uncertainties: concepts, approaches, gaps, and opportunities. Sustainability 13, 6738. https://doi.org/10.3390/su13126738
Smith, M., Allen, R., & Pereira, L. (1997). Revised FAO Methodology for Crop Water Requirements. Land and Water Development Division. FAO, Rome.
Sudheer, K. P., Gosain, A. K., & Ramasastri, K. S. (2003). Estimating actual evapotranspiration from limited climatic data using neural computing technique. Journal of Irrigation and Drainage Engineering 129, 214–218. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:3(214)
Thongkao, S., Ditthakit, P., Pinthong, S., Salaeh, N., Elkhrachy, I., Linh, N.T., & Pham, Q. B. (2022). Estimating FAO Blaney-Criddle b-Factor Using Soft Computing Models. Atmosphere 13, 1536. https://doi.org/10.3390/atmos13101536
Trajkovic, S., Todorovic, B., & Stankovic, M. 2003. Forecasting of reference evapotranspiration by artificial neural networks. Journal of Irrigation and Drainage Engineering 1296, 454–457. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:6(454)
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375
Van Eck, N.J., & Waltman, L. (2019). VOSviwer Manual version 1.6.10. CWTS Meaningful Metrics. Leiden, The Netherlands. 51 pp.
Velasco-Muñoz, J. F., Aznar-Sánchez, J. A., Belmonte-Ureña, L. J., & López-Serrano, M. J. (2018). Advances in water use efficiency in agriculture: A bibliometric analysis. Water, 10(4), 377. https://doi.org/10.3390/w10040377
Yarime, M., Takeda, Y., & Kajikawa, Y. (2010). Towards institutional analysis of sustainability science: A quantitative examination of the patterns of research collaboration. Sustainability Science Sci. 5, 115–125. https://doi.org/10.1007/s11625-009-0090-4
Yassin, M. A., Alazba, A. A., & Mattar, M. A. (2016). Modelling daily evapotranspiration using artificial neural networks under hyper arid conditions. Pakistan Journal of Agricultural Research 53(3), 695–712. https://doi.org/10.21162/PAKJAS/16.3179
Zhang, Y., Zhang, Y., Shi, K., & Yao, X. (2017). Research development, current hotspots, and future directions of water research based on MODIS images: A critical review with a bibliometric analysis. Environmental Science and Pollution Research 24, 15226–15239. https://doi.org/10.1007/s11356-017-9107-1
Zhou, X., Zhang, Y., Porter, A. L., Guo, Y., & Zhu, D. (2014). A patent analysis method to trace technology evolutionary pathways. Scientometrics 100, 705–721. https://doi.org/10.1007/s11192-014-1317-4
