Multi Criteria Decision Making Results Integration with Borda, Copeland, and Averaging Methods in Precise ET0 Method Determination

Laleh Parviz; Neda Azizi

doi:10.56093/aaz.v64i2.158986

Authors

Laleh Parviz Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran
Neda Azizi Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran

https://doi.org/10.56093/aaz.v64i2.158986

Keywords:

Climate, evaporation and transpiration, Integration, Multi- criteria decision-making

Abstract

Demand control in agriculture enhances water efficiency by optimizing resource utilization. Among the primary causes of water loss are evaporation and transpiration, making their accurate estimation crucial for effective water management. This study aims to determine the most precise potential evaporation and transpiration (ET0) estimation method using data from 18 synoptic stations. The methods evaluated include Ivanov, Thornthwaite, Blaney-Criddle (BC), Priestley-Taylor, Makkink, Turc, and Hargreaves- Samani (HS). Two analytical approaches were employed: evaluation criteria and multi-criteria decision-making (MCDM) techniques, incorporating the Analytic Hierarchy Process (AHP), VIKOR, and Shannon entropy methods. The rankings derived from these methods were further refined through integration techniques such as averaging, Copeland, and Borda methods. Among the evaluated methods, HS and BC performed best based on evaluation criteria. The relative root mean square error (RMSE) reduction from HS to other methods was 82.92% for Ivanov, 72.45% for Thornthwaite, 39.16% for BC, 37.39% for Priestley-Taylor, 26.64% for Makkink, and 68.54% for Turc. Notably, the Shannon entropy and AHP rankings aligned, consistently placing BC and HS at the top. In integrated ranking approaches, the Copeland and Borda methods yielded identical results, with BC, HS, Makkink, and Turc achieving high rankings. Priestley-Taylor and Ivanov were ranked equally in the averaging method, whereas Thornthwaite ranked lowest. The Ivanov method consistently placed last in Copeland and Borda rankings. Considering similarity criteria values exceeding 0.8, the BC method is particularly effective in wet and semi-arid climates, while the HS method demonstrates reliability across all three climate zones studied.

Downloads

Download data is not yet available.

References

Allen, R.G., Pereira, L.S., Raes, D. and Smith, M.1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9), p.D05109.

Allen, R.G., Pruitt, W.O., Raes, D., Smith, M. and Pereira, L.S. 2005. Estimating evaporation from bare soil and the crop coefficient for the initial period using common soil information. Journal of Irrigation and Drainage Engineering 131(1): 14-23. https://doi.org/10.1061/(ASCE)0733- 9437(2005)131:1(14).

Almorox, J., Quej, V.H. and Martí, P. 2015. Global performance ranking of temperature-based approaches for evapotranspiration estimation considering Köppen climate classes. Journal of Hydrology 528: 514-522. https://doi.org/10.1016/j. jhydrol.2015.06.057.

Dai, L., Fu, R., Zhao, Z., Guo, X., Du, Y., Hu, Z. And Cao, G. 2022. Comparison of fourteen reference evapotranspiration models with lysimeter measurements at a site in the humid alpine meadow, Northeastern Qinghai-Tibetan plateau. Frontiers in Plant Science 13:854196. https://doi.org/10.3389/fpls.2022.854196.

Dai, X. 2016. Dam site selection using an integrated method of AHP and GIS for decision-making support in Bortala, Northwest China. Master’s thesis, GEM thesis series nr 14, University of Twente, Netherlands and University of Lund, Sweden, 58p.

De Brito, M.M. and Evers, M. 2016. Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Natural Hazards and Earth System Sciences 16(4): 1019- 1033. https://doi.org/10.5194/nhess-16-1019-2016.

Djaman, K., Balde, A.B., Sow, A., Muller, B., Irmak, S., N’Diaye, M.K., Manneh, B., Moukoumbi, Y.D., Futakuchi, K. and Saito, K. 2015. Evaluation of sixteen reference evapotranspiration methods under Sahelian conditions in the Senegal River Valley. Journal of Hydrology: regional studies 3: 139-159. https://doi.org/10.1016/j.ejrh.2015.02.002.

Feng, Y., Jia, Y., Cui, N., Zhao, L., Li, C. and Gong, D. 2017. Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China. Agricultural Water Management 181: 1-9. https://doi.org/10.1016/j.agwat.2016.11.010.

Hadria, R., Benabdelouhab, T., Lionboui, H. and Salhi, A. 2021. Comparative assessment of different reference evapotranspiration models towards a fit calibration for arid and semi-arid areas. Journal of Arid Environments 184:104318. https://doi.org/10.1016/j.jaridenv.2020.104318.

Jarchi Eterabad, E. and Khashei, A. 2015. Evaluation of the best experimental method to estimate the amount of potential evaporation and transpiration at the same altitude. National Irrigation and Drainage Congress.

Lotfi, F.H. and Fallahnejad, R. 2010. Imprecise Shannon’s entropy and multi-attribute decision- making. Entropy, 12(1): 53-62. https://doi.org/10.3390/e12010053.

Mahdavi, A. and ZareAbyaneh, H. 2014. Selection of the Most Suitable Method for Estimation and Interpolation of Potential Evapotranspiration in Isfahan Province. Iranian Journal of Irrigation & Drainage, 8(3): 528-539.

Narayanamoorthy, S., Annapoorani, V., Kalaiselvan, S. and Kang, D. 2020. Hybrid hesitant fuzzy multi-criteria decision-making method: a symmetric analysis of the selection of the best water distribution system. Symmetry, 12(12): 2096. https://doi.org/10.3390/sym12122096.

Park, J., Baik, J. and Choi, M. 2017. Satellite-based crop coefficient and evapotranspiration using surface soil moisture and vegetation indices in Northeast Asia. Catena 156: 305-314. https://doi.org/10.1016/j.catena.2017.04.013.

Phan, T.T and Nguyen, X.H. 2020. Combining statistical machine learning models with ARIMAfor water level forecasting: The case of the Red River. Advances in Water Resources 142: 103656. https://doi.org/10.1016/j.advwatres.2020.103656.

Rajput, J., Singh, M., Lal, K., Khanna, M., Sarangi, A., Mukherjee, J. and Singh, S. 2024. Selection of alternate reference evapotranspiration models based on multi-criteria decision ranking for semiarid climate. Environment, Development and Sustainability 26(5): 11171-11216. https://doi.org/10.1007/s10668-023-03234-9.

Saaty, T.L. 2001. Fundamental of the analytic hierarchy process. The analytic hierarchy process in nature resources and environmental decision making 15-35. https://doi.org/10.1007/978-94-015-9799-9-2.

Sharma, G., Singh, A. and Jain, S. 2022. DeepEvap: Deep reinforcement learning-based ensemble approach for estimating reference evapotranspiration. Applied Soft Computing 125: 109113. https://doi.org/10.1016/j.asoc.2022.109113.

Shirmohammadi-Aliakbarkhani, Z. and Saberali, S.F. 2020. Evaluating of eight evapotranspiration estimation methods in arid regions of Iran. Agricultural Water Management 239: 106243. https://doi.org/10.1016/j.agwat.2020.106243.

Shu, Z., Zhou, Y., Zhang, J., Jin, J., Wang, L., Cui, N., Wang, G., Zhang, J., Wu, H., Wu, Z. and Chen, X. 2022. Parameter regionalization based on machine learning optimizes the estimation of reference evapotranspiration in data-deficient areas. Science of the Total Environment 844: 157034. https://doi.org/10.1016/j.scitotenv.2022.157034.

Wibawa, A.P., Fauzi, J.A., Isbiyantoro, S., Irsyada, R. and Hernández, L. 2019. VIKOR multi-criteria decision making with AHP reliable weighting for article acceptance recommendation. International Journal of Advances in Intelligent Informatics 5(2): 160-168.http://dx.doi.org/10.26555/ijain.v5i2.172.