Development of multiple linear regression models for annual reference evapotranspiration estimation under limited data conditions

Ahmadpari, Hedieh; Khaustov, Vitaly; Amini, Ata

doi:10.22098/mmws.2025.18810.1726

دانشگاه محقق اردبیلی

تعداد نشریات	31
تعداد شماره‌ها	484
تعداد مقالات	4,286
تعداد مشاهده مقاله	7,298,693
تعداد دریافت فایل اصل مقاله	4,896,200

	Development of multiple linear regression models for annual reference evapotranspiration estimation under limited data conditions
مدل سازی و مدیریت آب و خاک
مقاله 11، دوره 6، شماره 2، 1405، صفحه 209-231 اصل مقاله (2.21 M)
نوع مقاله: Special Issue: New Approaches to Water and Soil Management and Modeling
شناسه دیجیتال (DOI): 10.22098/mmws.2025.18810.1726
نویسندگان
Hedieh Ahmadpari¹؛ Vitaly Khaustov²؛ Ata Amini^* ³
¹Ph.D. Candidate, Hydrology of Land, Water Resources, Hydrochemistry, Russian State Hydrometeorological University, Saint Petersburg, Russia
²Candidate of Technical Sciences, Associate Professor at the Department of Engineering Hydrology of the RSHU, Saint Petersburg, Russia
³Professor, Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
چکیده
Accurate estimation of reference evapotranspiration (ET₀) is essential for agricultural water management, particularly in regions with limited data availability. The aim of this study was to evaluate multiple linear regression (MLR) models to estimate ET₀ at the annual scale. Meteorological data from the Kuhdasht synoptic station, Iran for a 25-year period (1998–2022) were used. ET₀ was calculated using the FAO-56 Penman-Monteith method implemented through the CROPWAT 8.0 software. A total of 31 MLR models were developed using the Regression option from the Analysis ToolPak of Microsoft Excel 2019 to quantify the relationship between ET₀ and climatic variables. Seven statistical indices were used to evaluate the performance of the MLR models in estimating ET₀. Results showed that 16 models achieved very high accuracy, with coefficients of determination (R²) greater than 0.92. Among single-variable models, wind speed ing up to 92% of ET₀ variability. Several two-variable models achieved R² = 0.92–0.96, and most three-variable models reached R² = 0.93–0.97. Four-variable models also performed strongly (R² ≈ 0.95–0.97), while the five-variable model yielded R² ≈ 0.97, similar to simpler models. Wind speed emerged as the most influential factor, highlighting that well-chosen two- or three-variable models can estimate ET₀ as effectively as more complex alternatives.
کلیدواژه‌ها
Reference evapotranspiration؛ FAO-56 Penman–Monteith؛ Multiple Linear Regression؛ Wind speed

مراجع
Ababakr, F. A., Ahmed, K. O., Amini, A., Karami Moghadam, M., & Gökçekuş, H. (2023). Spatio-temporal variations of groundwater quality index using geostatistical methods and GIS. Applied Water Science, 13(10), 206. doi: 10.1007/s13201-023-02010-4 Ahmadpari, H., & Khaustov, V. (2025a). Analyzing meteorological and hydrological droughts in the Darreh Dozdan River Basin through drought indices. Environment and Water Engineering, 11(2), 174-184.doi: 10.22034/ewe.2025.506959.2004 Ahmadpari, H., & Khaustov, V. (2025b). Agricultural drought monitoring using meteorological indices in Darreh Dozdan Basin, Iran. Advances in Civil Engineering and Environmental Science, 2(2), 72-84. doi: 10.22034/acees.2025.512324.1022 Ahmadpari, H., Hashemi Garmdareh, S. E., & Ghalehkohneh, K. (2017). Comparison of different methods of estimating potential evapotranspiration by FAO Penman Monteith (Case Study: Sepidan Region). Nivar, 41(98-99), 13-22. doi: 10.30467/nivar.2017.51886 Ahmadpari, H., Safavi Gerdini, M., & Ebrahimi, M. (2019). An appropriate method for estimating potential evapotranspiration in the absence of meteorological data (The case study of Khorrambid Township in Fars Province). Land Management Journal, 7(2), 223-230. doi: 10.22092/lmj.2019.120559 Ahmadpari, H., Shokoohi, E. S., Lalabadi, N. M., Gerdini, M. S., & Ebrahimi, M. (2019a). Assessment of potential evapotranspiration estimation methods in the fasa region. Specialty journal of agricultural sciences, 5(2-2019), 56-66. Alazba, A. A., Mattar, M. A., El-Shafei, A., Radwan, F., Ezzeldin, M., & Alrdyan, N. (2025). Daily Reference Evapotranspiration Derived from Hourly Timestep Using Different Forms of Penman–Monteith Model in Arid Climates. Water, 17(15), 2272. doi: 10.3390/w17152272 Amini, A. (2020). The role of climate parameters variation in the intensification of dust phenomenon. Natural Hazards, 102(1), 445–468. doi: 10.1007/s11069-020-03933-w Amini, A., Abdeh Kolahchi, A., Al-Ansari, N., Moghadam, M. K., & Mohammad, T. (2019). Application of TRMM precipitation data to evaluate drought and its effects on water resources instability. Applied Sciences (Switzerland), 9(24), 5377. MDPI. Amini, A., Arya, A., Eghbalzadeh, A., & Javan, M. (2017). Peak flood estimation under overtopping and piping conditions at Vahdat Dam, Kurdistan Iran. Arabian Journal of Geosciences, 10(6), 127. doi: 10.1007/s12517-017-2854-y Amini, A., & Hesami, A. (2017). The role of land use change on the sustainability of groundwater resources in the eastern plains of Kurdistan, Iran. Environmental Monitoring and Assessment, 189(6), 297. doi: 10.1007/s10661-017-6014-3 Binesh, S., Ahmadpari, H., Shayegh, E., Masoumi, M., & Vakili Tajareh, F. (2020). Preparation of spatial distribution maps of saffron water requirement in Kermanshah province. International Journal of Engineering and Technology, 12(2), 321-336.‏ doi: 10.21817/ijet/2020/v12i2/201202118 Brooks, G. P., & Ruengvirayudh, P. (2016). Best-subset selection criteria for multiple linear regression. General Linear Model Journal, 42(2), 14-25. Cochran, W. G. (1934). The distribution of quadratic forms in a normal system, with applications to the analysis of covariance. In Mathematical Proceedings of the Cambridge Philosophical Society (Vol. 30, No. 2, pp. 178-191). Cambridge University Press. da Silva, H. J., dos Santos, M. S., Junior, J. B. C., & Spyrides, M. (2016). Modeling of reference evapotranspiration by multiple linear regression. Journal of Hyperspectral Remote Sensing, 6(1), 44-58. doi: 10.5935/2237-2202.20160005 Das, B. K., Jha, D. N., Sahu, S. K., Yadav, A. K., Raman, R. K., & Kartikeyan, M. (2022). Analysis of variance (ANOVA) and design of experiments. In Concept Building in Fisheries Data Analysis (pp. 119-136). Singapore: Springer Nature Singapore. doi: 10.1007/978-981-19-4411-6_7 Dehghani, T., Liaghat, A., Rezaei Rad, H., & Ahmadpari, H. (2024). Estimating grain corn yield based on Landsat 8 satellite images (Case study: agricultural and industrial lands of Shahid Beheshti, Dezful). Iranian Journal of Irrigation & Drainage, 18(3), 433-447. Dimitriadou, S., & Nikolakopoulos, K. G. (2022). Multiple linear regression models with limited data for the prediction of reference evapotranspiration of the Peloponnese, Greece. Hydrology, 9(7), 124. doi: 10.3390/hydrology9070124 Djaman, K., Rudnick, D., Mel, V.C., Mutiibwa, D., Diop, L., Sall, M., Kabenge, I., Bodian, A., Tabari, H. and Irmak, S. (2017). Evaluation of Valiantzas’ simplified forms of the FAO-56 Penman-Monteith reference evapotranspiration model in a humid climate. Journal of Irrigation and Drainage Engineering, 143(8), 06017005. doi: 10.1061/(ASCE)IR.1943-4774.0001191 Draper, N. R., & Smith, H. (1998). Applied regression analysis. McGraw-Hill. Inc. doi: 10.1002/9781118625590.ch15 Jiang, Y., & Sun, W. (2025). Day-ahead electricity price prediction and error correction method based on feature construction–singular spectrum analysis–long short-term memory. Energies, 18(4), 1-22.‏ doi: 10.3390/en18040919 Karch, J. (2020). Improving on adjusted R-squared. Collabra: Psychology, 6(1), 45. doi: 10.1525/collabra.343 Karuppanan, S., Ramasamy, S., Lakshminarayanan, B., & Anuthaman, S. N. (2025). An effective machine learning model for the estimation of reference evapotranspiration under data-limited conditions. Research in Agricultural Engineering, 71(1), 22-37. doi: 10.17221/101/2023-RAE Khadempour, F., Bakhtiari, B., & Golestani, S. (2017). Sensitivity Analysis of FAO Penman-Monteith Model in Daily Reference Evapotranspiration Estimation and Zoning Sensitivity Coefficients across Iran. Water and Soil, 31(4), 1046-1059. doi: 10.22067/jsw.v31i4.57602 Kim, H. Y. (2014). Analysis of variance (ANOVA) comparing means of more than two groups. Restorative dentistry & endodontics, 39(1), 74. doi: 10.5395/rde.2014.39.1.74 Koç, D. L., & Can, M. E. (2023). Reference evapotranspiration estimate with missing climatic data and multiple linear regression models. PeerJ, 11, e15252. doi: 10.7717/peerj.15252 Liu, D., Wang, Z., Wang, L., Chen, J., Li, C., & Shi, Y. (2024). Improved remote sensing reference evapotranspiration estimation using simple satellite data and machine learning. Science of the Total Environment, 947, 174480. doi: 10.1016/j.scitotenv.2024.174480 Miller, R. L., Acton, C., Fullerton, D. A., Maltby, J., & Campling, J. (2002). Analysis of variance (ANOVA). In SPSS for social scientists (pp. 145-154). London: Macmillan Education UK. doi: 10.1007/978-0-230-62968-4_8 Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons. Purohit, R. C., Ingle, P. M., Bhakar, S. R., Mittal, H. K., Jain, H. K., Jain, H. K., & Singh, P. K. (2016). Impact of different meteorological parameters and relationship with short crop reference evapotranspiration for humid climatic conditions. International Research Journal of Earth Sciences, 4(8), 1-4. Research Office of the Iran Water Resources Management Company. (2012). Guidelines and criteria for classification and coding of basins and study areas in Iran (No. 310). Department of Technical Affairs of the Ministry of Energy, Vice Presidency for Strategic Planning and Supervision. Salahudin, H., Shoaib, M., Albano, R., Inam Baig, M.A., Hammad, M., Raza, A., Akhtar, A. and Ali, M.U. (2023). Using ensembles of machine learning techniques to predict reference evapotranspiration (et0) using limited meteorological data. Hydrology, 10(8), 169. doi: 10.3390/hydrology10080169 Stanley, R. P. (2011). Enumerative Combinatorics, Volume 1, Cambridge University Press, 725 pages. Sureiman, O., & Mangera, C. M. (2020). F-test of overall significance in regression analysis simplified. Journal of the Practice of Cardiovascular Sciences, 6(2), 116-122. doi: 10.4103/jpcs.jpcs_18_20 Taheri, M., Bigdeli, M., Imanian, H., & Mohammadian, A. (2025). An Overview of Evapotranspiration Estimation Models Utilizing Artificial Intelligence. Water, 17(9), 1384. doi: 10.3390/w17091384 Tegos, A., Efstratiadis, A., Malamos, N., Mamassis, N., & Koutsoyiannis, D. (2015). Evaluation of a parametric approach for estimating potential evapotranspiration across different climates. Agriculture and Agricultural Science Procedia, 4, 2-9. doi: 10.1016/j.aaspro.2015.03.002 Usta, S., & Gençoğlan, S. (2019). Estimation of reference evapotranspiration using multiple linear regression models. International Journal of Scientific and Technological Research, 5(2), 5-19. doi: 10.7176/jstr/5-2-02 Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2022). Probability and statistics for engineers and scientists. London, UK: Pearson. Yirga, S. A. (2019). Modelling reference evapotranspiration for Megecha catchment by multiple linear regression. Modeling Earth Systems and Environment, 5(2), 471-477. doi: 10.1007/s40808-019-00574-2
آمار تعداد مشاهده مقاله: 340 تعداد دریافت فایل اصل مقاله: 122

سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب

پیوندهای مفید

آمار

Development of multiple linear regression models for annual reference evapotranspiration estimation under limited data conditions