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Statistical and multi-criteria methods for preprocessing meteorological data in reference evapotranspiration | ||
| مدل سازی و مدیریت آب و خاک | ||
| مقاله 15، دوره 5، ویژه نامه 1، 1404، صفحه 252-269 اصل مقاله (1.3 M) | ||
| نوع مقاله: Special issue on "Climate Change and Effects on Water and Soil" | ||
| شناسه دیجیتال (DOI): 10.22098/mmws.2025.18259.1669 | ||
| نویسندگان | ||
| Laleh Parviz* 1؛ Fardin Ghanbari-Maleki2 | ||
| 1Associate Professor, Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran | ||
| 2M.Sc Student, Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran | ||
| چکیده | ||
| The capability of reference evapotranspiration (ET0) in water loss consideration benefits irrigation planning, agricultural studies, and water resources management. The requirement for effective meteorological data determination is noteworthy in the ET0 estimation, in which Pearson, Kendall’s tau-b correlation coefficients, the standardized Beta coefficient, stepwise regression (in statistical approach), simple additive weighting with fuzzy normalization (F-SAW), and Shannon’s entropy (multi-criteria) were considered as the preprocessing methods. Different combinations of meteorological data in 11 synoptic stations in Iran were applied to test the performance of the methods. The first three statistical methods yield similar results. Root mean square error decreasing from Pearson and stepwise regression to F-SAW is 15.4% and 14.7%, respectively; therefore, the F-SAW could enhance the ET0 simulations. The differences between the two preprocessing methods based on the MCDM approach in all 132 cases are low, except for 34 cases with better performance of F-SAW. F-SAW performance investigation among all stations on annual and monthly scales stated that Zanjan and Semnan stations, with Nash-Sutcliffe efficiency equal to 0.97 and 0.99, respectively, have better performance than the other stations. The temperature, humidity, wind speed, and solar radiation were obtained as the effective data. In the decision-making analysis with high efficiency, the procedure of assigning weights to the criteria has an important role, and the high performance of F-SAW can be linked to the normalization structure. In the different climates of stations, the performance of each dataset is distinctive; therefore, an efficient preprocessing method can upgrade the adequacy of ET0 estimation. | ||
| کلیدواژهها | ||
| Preprocessing؛ Reference evapotranspiration (ET0)؛ Fuzzy normalization؛ Combinations | ||
| مراجع | ||
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References Gong, A. M. (2016). Using automatic calibration method for optimizing the performance of Pedotransfer functions of saturated hydraulic conductivity. Ain Shams Engineering Journal, 7(2), 653-662. doi: 10.1016/j.asej.2015.05.012 Ahmadi, F., Mehdizadeh, S., Mohammadi, B., Pham, Q. B., Doan, T. N. C., Vo, N. D. (2021). Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation. Agricultural Water Management, 244, 106622. doi: 10.1016/j.agwat.2020.106622 Ahmadpari, H., Khaustov, V. (2025). 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 Berti, A., Tardivo, G., Chiaudani, A., Rech, F., Borin, M. (2014). Assessing reference evapotranspiration by the Hargreaves method in north-eastern Italy. Agricultural Water Management, 140, 20-5. doi: 10.1016/j.agwat.2014.03.015 Butchart-Kuhlmann, D., Kralisch, S., Fleischer, M., Meinhardt, M., Brenning, A. (2018). Multicriteria decision analysis framework for hydrological decision support using environmental flow components. Ecological Indicators, 93, 470-480. doi:10.1016/j.ecolind.2018.04.057 Cai, W., Wen, X., Li, C., Shao, J., Xu, J. (2023). Predicting the energy consumption in buildings using the optimized support vector regression model. Energy, 273, 127188. doi: 10.1016/j.energy.2023.127188 Chauhan, S., Shrivastava, R. K. (2009). Performance evaluation of reference evapotranspiration estimation using climate based methods and artificial neural networks. Water Resources Management, 23(5), 825-837. doi: 10.1007/s11269-008-9301-5 De Martonne, E. (1925). TraitéGéographie. Physique: 3 tomes. Max leclcrc and H. Bourrclier, proprietors of LibrairicArmard Colin: Paris. Dooley, A.E., Smeaton, D. C., Sheath, G. W., Ledgard, S. F. (2009). Application of multiple criteria decision analysis in the New Zealand agricultural industry. The Journal of Multi-Criteria Decision Analysis, 16(1‐2), 39-53. doi: 10.1002/mcda.437 Dwivedi, P. P., Sharma, D. K. (2022a). Application of Shannon Entropy and COCOSO techniques to analyze performance of sustainable development goals: The case of the Indian Union Territories. Results in Engineering, 14, 100416. doi: 10.1016/j.rineng.2022.100416 Dwivedi, P. P., Sharma, D. K. (2022b). Application of Shannon entropy and CoCoSo methods in selection of the most appropriate engineering sustainability components. Cleaner Materials, 5, 100118. doi: 10.1016/j.clema.2022.100118 Ellenburg, W. L., Cruise, J., Singh, V. P. (2017). The Role of Evapotranspiration in Streamflow Modeling-an Analysis Using Entropy Theory. In AGU Fall Meeting Abstracts 2017 Dec (Vol. 2017, pp. H23C-1677). Fu, T., Li, X., Jia, R., Feng, L. (2021). A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland. Journal of Hydrology, 603, 126881. doi: 10.1016/j.jhydrol.2021.126881 Ghabaei Sough, M., Mosaedi, A., Hesam, M., Hezarjaribi, A. (2010). Evaluation Effect of Input Parameters Preprocessing in Artificial Neural Networks (Anns) by Using Stepwise Regression and Gamma Test Techniques for Fast Estimation of Daily Evapotranspiration. Water and Soil, 24(3), 610-624. doi: 10.22067/jsw.v0i0.3631 Gong, D., Hao, W., Gao, L., Feng, Y., Cui, N. (2021). Extreme learning machine for reference crop evapotranspiration estimation: Model optimization and spatiotemporal assessment across different climates in China. Computers and Electronics in Agriculture, 187, 106294. doi: 10.1016/j.compag.2021.106294 Haoyuan, S., Yizhong, M., Chenglong, L., Jian, Z., Lijun, L. (2023). Hierarchical Bayesian support vector regression with model parameter calibration for reliability modeling and prediction. Reliability Engineering and System Safety, 229, 108842. doi: 10.1016/j.ress.2022.108842 Hu, X., Shi, L., Lian, X., Bian, J. (2023). Parameter variability across different timescales in the energy balance-based model and its effect on evapotranspiration estimation. Science of the Total Environment, 871, 161919. doi: 10.1016/j.scitotenv.2023.161919. Jiang, G. J., Chen, H. X., Sun, H. H., Yazdi, M., Nedjati, A., Adesina, K. A. (2021). An improved multi-criteria emergency decision-making method in environmental disasters. Soft Computing, 25(15), 10351-10379. doi: 10.1007/s00500-021-05826-x Kim, H. J., Chandrasekara, S., Kwon, H. H., Lima, C., Kim, T. W. (2023). A novel multi-scale parameter estimation approach to the Hargreaves-SamaniEq. for estimation of Penman-Monteith reference evapotranspiration. Agricultural Water Management, 275, 108038. doi: 10.1016/j.agwat.2022.108038 Malek, M. H., Berger, D. E., Coburn, J. W. (2007). On the inappropriateness of stepwise regression analysis for model building and testing. European Journal of Applied Physiology, 101, 263-264. doi: 10.1007/s00421-007-0485-9 Maroufpoor, S., Bozorg-Haddad, O., Maroufpoor, E. (2020). Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligent model: Hybridization of artificial neural network with grey wolf optimizer algorithm. Journal of Hydrology, 588, 125060. doi: 10.1016/j.jhydrol.2020.125060 Musbah, H., Ali, G., Aly, H.H., Little, T. A. (2022). Energy management using multi-criteria decision making and machine learning classification algorithms for intelligent system. Electric Power Systems Research, 203, 107645. doi: 10.1016/j.epsr.2021.107645 Nieminen, P. (2022). Application of standardized regression coefficient in meta-analysis. BioMedInformatics, 2(3), 434-458. doi:10.3390/biomedinformatics2030028 Raffinetti, E., Aimar, F. (2019). MDCgo takes up the association/correlation challenge for grouped ordinal data. AStA Advances in Statistical Analysis, 103(4), 527-561. doi: 10.1007/s10182-018-00341-1 Rezaei, I., Amirshahi, S. H., Mahbadi, A. A. (2023). Utilizing support vector and kernel ridge regression methods in spectral reconstruction. Results in Optics, 11, 100405. doi: 10.1016/j.rio.2023.100405 Saroughi, M., Mirzania, E., Achite, M., Katipoğlu, O. M., Al-Ansari, N., Vishwakarma, D. K., Chung, I. M., Alreshidi, M. A., Yadav, K. K. (2024). Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran). Heliyon, 10(7). doi: 10.1016/j.heliyon.2024.e29006 Shternshis, A., Mazzarisi, P., Marmi, S. (2022). Measuring market efficiency: The Shannon entropy of high-frequency financial time series. Chaos, Solitons & Fractals, 162, 112403. doi: 10.1016/j.chaos.2022.112403 Shu, Z., Zhou, Y., Zhang, J., Jin, J., Wang, L., Cui, N., Wang, G., Zhang, J., Wu, H., Wu, Z., Chen, X. (2022). Parameter regionalization based on machine learning optimizes the estimation of reference evapotranspiration in data deficient area. Science of the Total Environment, 844, 157034. doi: 10.1016/j.scitotenv.2022.157034 Smith, G. (2018). Step away from stepwise. Journal of Big Data, 5(32), 1-12. doi: 10.1186/s40537-018-0143-6 Su, Q., Singh, V. P., Karthikeyan, R. (2022). Improved reference evapotranspiration methods for regional irrigation water demand estimation. Agricultural Water Management, 274, 107979. doi: 10.1016/j.agwat.2022.107979 Tabar, H., Hosseinzadeh Talaee, P. (2013). Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Computing & Applications, 23, 341-348. doi: 10.1007/s00521-012-0904-7 Yadeta, D., Kebede, A., Tessema, N. (2020). Potential evapotranspiration models evaluation, modelling, and projection under climate scenarios, Kesem sub-basin, Awash River basin, Ethiopia. Modeling Earth System and Environment, 6, 2165-2176. doi: 10.1007/s40808-020-00831-9 Yao, Y., Mallik, A. U. (2022). Estimation of actual evapotranspiration and water stress in the Lijiang River Basin, China using a modified Operational Simplified Surface Energy Balance (SSEBop) model. Journal of Hydro-environment Research, 41, 1-11. doi: 10.1016/j.jher.2022.01.003 Zhu, N., Wang, J., Luo, D. (2024). Unveiling evapotranspiration patterns and energy balance in a subalpine forest of the Qinghai–Tibet Plateau: observations and analysis from an eddy covariance system. Journal of Forestry Research, 35, 53. doi: 10.1007/s11676-024-01708-8
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