Anfis Based Reference Evapotranspiration (ET0) Estimation Using Limited and Different Climate Parameters
Abstract views: 80
/ 
PDF downloads: 43
DOI:
https://doi.org/10.5281/zenodo.13761632Keywords:
Machine learning method, water management, maximum temperature, air density, solar energyAbstract
This study aims to estimate the reference evapotranspiration (ET0) with adaptive neuro-fuzzy inference system (ANFIS). The calculation of ET0 requires climate data such as maximum and minimum temperature, wind speed, sunshine duration and humidity. Sometimes it may not be possible to access the climate data. In the study, ET0 is estimated with ANFIS by using fewer input parameters. Moreover, it is proved that it is possible to calculate ET0 via ANFIS by using some other climate parameters such as temperature-humidity-wind index (THW), air pressure, wind chill, which have no direct effect on the calculation of ET0. In the study, five scenarios were created and evaluated with a statistical performance indicator. In Scenario 1, ET0 was estimated using THW, air pressure and air density. The relationship between the ET0 estimated by ANFIS and the calculated ET0 was found to be 0.76 (R2). In Scenario 2, THW, solar energy and solar radiation were used and R2 was found to be 0.66. Scenario 3 used THW and wind chill, resulting in an R2 of 0.43. In Scenario 4, THW and maximum temperature were used and R2 was 0.87. In Scenario 5, THW and humidity were used and the R2 with ET0 was 0.84.
References
Adnan, R.M., Mostafa, R.R., Islam, A.R.M.T., Kisi, O., Kuriqi, A., Heddam, S., 2021. Estimating reference evapotranspiration using hybrid adaptive fuzzy inferencing coupled with heuristic algorithms. Computers and Electronics in Agriculture, 191: 106541.
Aghelpour, P., Norooz-Valashedi, R., 2022. Predicting daily reference evapotranspiration rates in a humid region, comparison of seven various data-based predictor models. Stochastic Environmental Research and Risk Assessment, 36(12): 4133-4155.
Alam, M.M., Akter, M.Y., Islam, A.R.M.T., Mallick, J., Kabir, Z., Chu, R., Arabameri, A., Pal, S.C., Al Masud, M.A., Costache, R., 2024. A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models. Journal of Environmental Management, 351: 119714.
Alizamir, M., Kisi, O., Muhammad Adnan, R., Kuriqi, A., 2020. Modelling reference evapotranspiration by combining neuro-fuzzy and evolutionary strategies. Acta Geophysica, 68: 1113-1126.
Allen, R.G., Pereira, L.S., Raes, D., Smith, M, 1998. Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, 300(9): D05109.
Araghi, A., Adamowski, J., Martinez, C.J., 2020. Comparison of wavelet-based hybrid models for the estimation of daily reference evapotranspiration in different climates. Journal of Water and Climate Change, 11(1): 39-53.
Bakhtiari, B., Mohebbi‐Dehaghani, A., Qaderi, K., 2022. Comparative analysis of data‐driven methods for daily reference evapotranspiration estimation of Southern Caspian Sea. Meteorological Applications, 29(4): e2091.
Bidabadi, M., Babazadeh, H., Shiri, J., Saremi, A., 2024. Estimation reference crop evapotranspiration (ET0) using artificial intelligence model in an arid climate with external data. Applied Water Science, 14 (1): 3.
Chia, M.Y., Huang, Y.F., Koo, C.H., 2021. Improving reference evapotranspiration estimation using novel inter-model ensemble approaches. Computers and Electronics in Agriculture, 187: 106227.
Cobaner, M., 2011. Evapotranspiration estimation by two different neuro-fuzzy inference systems, Journal of Hydrology, 398 (3-4): 292-302.
Del Cerro, R.T.G., Subathra, M., Kumar, N.M., Verrastro, S., George, S.T., 2021. Modelling the daily reference evapotranspiration in semi-arid region of South India: A case study comparing ANFIS and empirical models. Information Processing in Agriculture, 8(1): 173-184.
Duhan, D., Singh, M.C., Singh, D., Satpute, S., Singh, S., Prasad, V., 2023. Modeling reference evapotranspiration using machine learning and remote sensing techniques for semi-arid subtropical climate of Indian Punjab. Journal of Water and Climate Change, 14(7): 2227-2243.
El-Kenawy, E.S.M., Zerouali, B., Bailek, N., Bouchouich, K., Hassan, M.A., Almorox, J., Kuriqi, A., Eid, M., Ibrahim, A., 2022. Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions. Environmental Science and Pollution Research, 29(54): 81279-81299.
Goyal, P., Kumar, S., Sharda, R., 2023. A review of the Artificial Intelligence (AI) based techniques for estimating reference evapotranspiration: Current trends and future perspectives. Computers and Electronics in Agriculture, 209: 107836.
Gupta, S., Kumar, P., Kishore, G., Ali, R., Al-Ansari, N., Vishwakarma, D.K., Kuriqi, A., Pham, Q.B., Kisi, O., Heddam, S., 2024. Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network. Applied Water Science, 14(6): 138.
Hadadi, F., Moazenzadeh, R., Mohammadi, B., 2022. Estimation of actual evapotranspiration: A novel hybrid method based on remote sensing and artificial intelligence. Journal of Hydrology, 609: 127774.
Kartal, V., 2024. Prediction of monthly evapotranspiration by artificial neural network model development with Levenberg–Marquardt method in Elazig, Turkey. Environmental Science and Pollution Research, 31(14): 20953-20969.
Katipoğlu, O.M., 2023. Combining discrete wavelet decomposition with soft computing techniques to predict monthly evapotranspiration in semi-arid Hakkâri province, Türkiye. Environmental Science and Pollution Research, 30(15): 44043-44066.
Keten, M., Değirmenci, H., 2020. Farklı sulama seviyeleri altında yetiştirilen ikinci ürün silajlık mısır ve sorgum bitkilerinin yaprak su potansiyellerinin karşılaştırılması. ISPEC Tarım Bilimleri Dergisi, 4(4): 865-874.
Kisi, O., Zounemat-Kermani, M., 2014. Comparison of two different adaptive neuro-fuzzy inference systems in modelling daily reference evapotranspiration. Water Resources Management, 28(9): 2655-2675.
Küçüktopcu, E., Cemek, E., Cemek, B., Simsek, H., 2023. Hybrid statistical and machine learning methods for daily evapotranspiration modeling. Sustainability, 15(7): 5689.
Ladlani, I., Houichi, L., Djemili, L., Heddam, S., Belouz, K., 2014. Estimation of daily reference evapotranspiration (ET 0) in the North of Algeria using adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) models: a comparative study. Arabian Journal for Science and Engineering, 39: 5959-5969.
Lu, Y., Li, T., Hu, H., Zeng, X., 2023. Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China. Agricultural Water Management, 279: 108175.
Mehdizadeh, S., Mohammadi, B., Pham, Q.B., Duan, Z., 2021. Development of boosted machine learning models for estimating daily reference evapotranspiration and comparison with empirical approaches. Water, 13(24): 3489.
Özdoğan Çavdar, T., Tepecik, M., Geren, H., 2021. A preliminary study on the effect of deficit irrigation application on the yield and some yield characteristics of burnet (Poterium sanguisorba) in early growth stage. ISPEC Journal of Agricultural Sciences, 5(2): 362–371.
Özel, A., Büyükyıldız, M., 2019, Aylık buharlaşma tahmininde yapay zeka yöntemlerinin kullanilabilirliği. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 8(1): 244-254.
Qin, A., Fan, Z., Zhang, L., 2024. Information entropy‐based hybrid models improve the accuracy of reference evapotranspiration forecast. Advances in Meteorology, 1: 9922690.
Quej, V.H., de la Cruz Castillo, C., Almorox, J., Rivera-Hernandez, B., 2022. Evaluation of artificial intelligence models for daily prediction of reference evapotranspiration using temperature, rainfall and relative humidity in a warm sub-humid environment. Italian Journal of Agrometeorology, 1: 49-63.
Rajagopal, M., Ponnuchamy, M., Kapoor, A., 2022. Water management for irrigation scheduling by computing evapotranspiration using ANFIS modelling. Desalination and Water Treatment, 251: 123-133.
Roy, D.K., Barzegar, R., Quilty, J., Adamowski, J., 2020. Using ensembles of adaptive neuro-fuzzy inference system and optimization algorithms to predict reference evapotranspiration in subtropical climatic zones. Journal of Hydrology, 591: 125509.
Roy, D.K., Lal, A., Sarker, K.K., Saha, K.K., Datta, B., 2021. Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system. Agricultural Water Management, 255: 107003.
Roy, D.K., Sarkar, T.K., Kamar, S.S.A., Goswami, T., Muktadir, M.A., Al-Ghobari, H.M., Alataway, A., Dewidar, A.Z., El-Shafei, A.A., Mattar, M.A., 2022. Daily prediction and multi-step forward forecasting of reference evapotranspiration using LSTM and Bi-LSTM models. Agronomy, 12(3): 594.
Roy, D., 2021. Long short-term memory networks to predict one-step ahead reference evapotranspiration in a subtropical climatic zone. Environmental Process 8: 911–941.
Seifi, A., Riahi, H., 2020, Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran. Journal of Water and Climate Change, 11(1): 217-240.
Seydoşoğlu, S., 2018. Bazı doğal mera alanlarının bitki örtüsü özellikleri, mera durumu ve sağlığının belirlenmesi. Türkiye Ormancılık Dergisi,19(4): 368-373.
Sharafi, S., Ghaleni, M.M., Scholz, M., 2023. Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran. Heliyon, 9(2): e13245
Terzi, Ö., Erol Keskin, M., Dilek Taylan, E., 2006. Estimating evaporation using ANFIS. Journal of Irrigation and Drainage Engineering, 132(5): 503-507.
Üneş, F., Kaya, Y.Z., Mamak, M., 2020. Daily reference evapotranspiration prediction based on climatic conditions applying different data mining techniques and empirical equations. Theoretical and Applied Climatology, 141: 763-773.
Valipour, M., Khoshkam, H., Bateni, S.M., Jun, C., Band, S.S., 2023, Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States. Agricultural Water Management, 283: 108311.
Wu, L., Peng, Y., Fan, J., Wang, Y., Huang, G., 2021. A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation. Agricultural Water Management, 245: 106624.
Yan, X., Yang, N., Ao, R., Mohammadian, A., Liu, J., Cao, H., Yin, P., 2023, Deep learning for daily potential evapotranspiration using a HS-LSTM approach. Atmospheric Research, 292: 106856.
Zeinolabedini Rezaabad, M., Ghazanfari, S., Salajegheh, M., 2020. ANFIS modeling with ICA, BBO, TLBO, and IWO optimization algorithms and sensitivity analysis for predicting daily reference evapotranspiration. Journal of Hydrologic Engineering, 25(8): 04020038.
Zereg, S., Belouz, K., 2024. Modeling daily reference evapotranspiration using SVR machine learning algorithm with limited meteorological data in Dar-el-Beidha, Algeria. Acta Geophysica, 72(3): 2009-2025.
Zhu, N., Wang, J., Luo, D., Wang, X., Shen, C., Wu, N., Zhang, N., Tian, B., Gai, A., 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(1): 53.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 The copyright of the published article belongs to its author.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
