Abstract
Forecasting evaporation, an important variable in the hydrological cycle, is crucial for managing water resources and taking precautions against severe phenomena, such as droughts and floods. In this study, the prediction of daily pan evaporation was carried out in the Euphrates sub-basin, Turkey, which has different climate characteristics and is a critical region for Turkey or neighbouring countries. In this regard, two empirical models, namely the Griffith model and calibrated Hargreaves-Samani, and four ensemble empirical mode decomposition (EEMD) based data-driven models, namely EEMD-Random Forests (EEMD-RF), EEMD-Artificial Neural Network (EEMD-ANN), EEMD-Gradient Boosting Machines (EEMD-GBM), and EEMD-Regression Tree (EEMD-RT) were used for evaporation forecasting. The EEMD and Recursive Feature Elimination (RFE) were implemented as a signal decomposition technique and determination of the importance of the EEMD components, respectively. Although the empirical models yielded satisfactory performance, they predicted low and high evaporation values poorly, in general. The EEMD-RF, EEMD-ANN, and EEMD-GBM models performed better than the EEMD-RT model. The data-driven models, except EEMD-RT, outperformed the empirical models, especially regarding predicting extreme evaporation values. The sensitivity analysis indicated that wind speed, humidity, and maximum temperature could influence evaporation forecasting. This study shows that using data-driven models benefitting from EEMD and RFE can be a good alternative to empirical models for predicting evaporation.
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Data are available from the Turkish State Meteorological Service.
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References
Abed M, Imteaz MA, Ahmed AN, Huang YF (2021) Application of long short-term memory neural network technique for predicting monthly pan evaporation. Sci Rep 11:1–19. https://doi.org/10.1038/s41598-021-99999-y
Abed M, Imteaz MA, Ahmed AN, Huang YF (2022) Modelling monthly pan evaporation utilising random forest and deep learning algorithms. Sci Rep 12:13132. https://doi.org/10.1038/s41598-022-17263-3
Abtew W, Melesse A (2013) Evaporation and evapotranspiration: measurements and estimations. Springer, Dordrecht
Ahmadi F, Mehdizadeh S, Mohammadi B (2021) Development of bio-inspired- and wavelet-based hybrid models for reconnaissance drought index modeling. Water Resour Manage 35:4127–4147. https://doi.org/10.1007/s11269-021-02934-z
Aires URV, Silva DD, da, Fernandes Filho EI et al (2023) Machine learning-based modeling of surface sediment concentration in Doce river basin. J Hydrol (Amst) 619:129320. https://doi.org/10.1016/j.jhydrol.2023.129320
Al-Mukhtar M (2019) Random forest, support vector machine, and neural networks to modelling suspended sediment in Tigris River-Baghdad. Environ Monit Assess 191:673. https://doi.org/10.1007/s10661-019-7821-5
Ali Ghorbani M, Kazempour R, Chau K-W et al (2018) Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran. Eng Appl Comput Fluid Mech 12:724–737. https://doi.org/10.1080/19942060.2018.1517052
Ayyadevara VK (2018) Gradient boosting machine. Pro Machine Learning Algorithms. Apress, Berkeley, CA, pp 117–134
Bojanowski J (2016) Sirad: functions for calculating daily solar radiation and evapotranspiration. R package version 2.3-3, 1–33. https://CRAN.R-project.org/package=sirad
Breiman L (1984) Classification and regression trees. Routledge, New York
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Cahoon JE, Costello TA, Ferguson JA (1991) Estimating pan evaporation using limited meteorological observations. Agric For Meteorol 55:181–190. https://doi.org/10.1016/0168-1923(91)90061-T
Drisya J, Kumar DS, Roshni T (2021) Hydrological drought assessment through streamflow forecasting using wavelet enabled artificial neural networks. Environ Dev Sustain 23:3653–3672. https://doi.org/10.1007/s10668-020-00737-7
Duarte VBR, Viola MR, Giongo M et al (2022) Streamflow forecasting in Tocantins river basins using machine learning. Water Supply 22:6230–6244. https://doi.org/10.2166/ws.2022.155
Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x
Elsawwaf M, Willems P, Feyen J (2010) Assessment of the sensitivity and prediction uncertainty of evaporation models applied to Nasser Lake, Egypt. J Hydrol (Amst) 395:10–22. https://doi.org/10.1016/j.jhydrol.2010.10.002
Emadi A, Zamanzad-Ghavidel S, Fazeli S et al (2021) Multivariate modeling of pan evaporation in monthly temporal resolution using a hybrid evolutionary data-driven method (case study: Urmia Lake and Gavkhouni basins). Environ Monit Assess 193:355. https://doi.org/10.1007/s10661-021-09060-8
Gaci S (2016) A new ensemble empirical Mode decomposition (EEMD) denoising method for seismic signals. Energy Procedia 97:84–91. https://doi.org/10.1016/j.egypro.2016.10.026
Gharaei-Manesh S, Fathzadeh A, Taghizadeh-Mehrjardi R (2016) Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran. Cold Reg Sci Technol 122:26–35. https://doi.org/10.1016/j.coldregions.2015.11.004
Gong Y, Wang Z, Xu G, Zhang Z (2018) A comparative study of groundwater level forecasting using data-driven models based on ensemble empirical mode decomposition. Water (Basel) 10:730. https://doi.org/10.3390/w10060730
Goyal MK, Ojha CSP (2012) Downscaling of precipitation on a lake basin: evaluation of rule and decision tree induction algorithms. Hydrol Res 43:215–230. https://doi.org/10.2166/nh.2012.040
Goyal MK, Bharti B, Quilty J et al (2014) Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, fuzzy logic, and ANFIS. Expert Syst Appl 41:5267–5276. https://doi.org/10.1016/j.eswa.2014.02.047
Gramacy RB (2007) Tgp: an R Package for bayesian nonstationary, semiparametric nonlinear regression and design by treed gaussian process models. J Stat Softw 19:1–46. https://doi.org/10.18637/jss.v019.i09
Gramacy RB, Taddy M (2010) Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R Package for Treed Gaussian process models. J Stat Softw 33:1–48. https://doi.org/10.18637/jss.v033.i06
Gramacy RB, Taddy M (2022) Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R package for treed Gaussian process models. https://cran.r-project.org/web/packages/tgp/vignettes/tgp2.pdf
Griffiths JF (1966) Another evaporation formula. Agric Meteorol 3:257–261. https://doi.org/10.1016/0002-1571(66)90033-1
Guan BT (2014) Ensemble empirical mode decomposition for analyzing phenological responses to warming. Agric For Meteorol 194:1–7. https://doi.org/10.1016/j.agrformet.2014.03.010
Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J Hydrol (Amst) 377:80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
Hargreaves GH, Samani ZA (1982) Estimating potential evapotranspiration. J Irrig Drain Div 108:225–230. https://doi.org/10.1061/JRCEA4.0001390
Hargreaves G, Samani Z (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99. https://doi.org/10.13031/2013.26773
He X, Luo J, Li P et al (2020) A hybrid model based on variational mode decomposition and gradient boosting regression tree for monthly runoff forecasting. Water Resour Manage 34:865–884. https://doi.org/10.1007/s11269-020-02483-x
Katipoğlu OM, Acar R (2022) Space-time variations of hydrological drought severities and trends in the semi-arid Euphrates Basin, Turkey. Stoch Env Res Risk Assess 36:4017–4040. https://doi.org/10.1007/s00477-022-02246-7
Keshtegar B, Piri J, Kisi O (2016) A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method. Comput Electron Agric 127:120–130. https://doi.org/10.1016/j.compag.2016.05.018
Khosravi K, Daggupati P, Alami MT et al (2019) Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: a case study in Iraq. Comput Electron Agric 167:105041. https://doi.org/10.1016/J.COMPAG.2019.105041
Kisi O (2009) Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks. Hydrol Process 23:213–223. https://doi.org/10.1002/HYP.7126
Kisi O, Heddam S (2019) Evaporation modelling by heuristic regression approaches using only temperature data. Hydrol Sci J 64:653–672. https://doi.org/10.1080/02626667.2019.1599487
Kumar M, Kumari A, Kumar D et al (2021) The superiority of data-driven techniques for estimation of daily pan evaporation. Atmos (Basel) 12:701. https://doi.org/10.3390/atmos12060701
Kushwaha NL, Rajput J, Elbeltagi A et al (2021) Data intelligence model and meta-heuristic algorithms-based pan evaporation modelling in two different agro-climatic zones: a case study from Northern India. Atmos (Basel) 12:1654. https://doi.org/10.3390/atmos12121654
Lu X, Ju Y, Wu L et al (2018) Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models. J Hydrol (Amst) 566:668–684. https://doi.org/10.1016/j.jhydrol.2018.09.055
Majhi B, Naidu D, Mishra AP, Satapathy SC (2020) Improved prediction of daily pan evaporation using Deep-LSTM model. Neural Comput Appl 32:7823–7838. https://doi.org/10.1007/s00521-019-04127-7
Malekian A, Chitsaz N (2021) Concepts, procedures, and applications of artificial neural network models in streamflow forecasting. In: Sharma P, Machiwal D (eds) Advances in streamflow forecasting. Elsevier, pp 115–147
Malik A, Saggi MK, Rehman S et al (2022) Deep learning versus gradient boosting machine for pan evaporation prediction. Eng Appl Comput Fluid Mech 16:570–587. https://doi.org/10.1080/19942060.2022.2027273
McMahon TA, Finlayson BL, Peel MC (2016) Historical developments of models for estimating evaporation using standard meteorological data. Wiley Interdiscip Rev Water 3:788–818. https://doi.org/10.1002/WAT2.1172
Moghaddamnia A, Ghafari Gousheh M, Piri J et al (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32:88–97. https://doi.org/10.1016/J.ADVWATRES.2008.10.005
Mohammadi B (2023) Modeling various drought time scales via a merged artificial neural network with a firefly algorithm. Hydrology 10:58. https://doi.org/10.3390/hydrology10030058
Moriasi DN, Gitau MW, Pai N, Daggupati P (2015) Hydrologic and water quality models: performance measures and evaluation criteria. Trans ASABE 58:1763–1785. https://doi.org/10.13031/trans.58.10715
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I — a discussion of principles. J Hydrol (Amst) 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobot 7. https://doi.org/10.3389/fnbot.2013.00021
Niu W, Feng Z, Zeng M et al (2019) Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm. Appl Soft Comput 82:105589. https://doi.org/10.1016/j.asoc.2019.105589
Nourani V, Tajbakhsh AD, Molajou A (2019) Data mining based on wavelet and decision tree for rainfall-runoff simulation. Hydrol Res 50:75–84. https://doi.org/10.2166/nh.2018.049
Ogunrinde AT, Oguntunde PG, Fasinmirin JT, Akinwumiju AS (2020) Application of artificial neural network for forecasting standardized precipitation and evapotranspiration index: A case study of Nigeria. Engineering Reports 2. https://doi.org/10.1002/eng2.12194
Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc Lond A Math Phys Sci 193:120–145. https://doi.org/10.1098/RSPA.1948.0037
Pham LT, Luo L, Finley A (2021) Evaluation of random forests for short-term daily streamflow forecasting in rainfall- and snowmelt-driven watersheds. Hydrol Earth Syst Sci 25:2997–3015. https://doi.org/10.5194/hess-25-2997-2021
Prasad R, Deo RC, Li Y, Maraseni T (2018) Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition. Geoderma 330:136–161. https://doi.org/10.1016/j.geoderma.2018.05.035
Priestly CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100:81–92. https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2
Rahimikhoob A (2009) Estimating daily pan evaporation using artificial neural network in a semi-arid environment. Theor Appl Climatol 98:101–105. https://doi.org/10.1007/s00704-008-0096-3
Rani A, Kumar N, Kumar J et al (2022) Machine learning for soil moisture assessment. In: Poonia RC, Singh V, Nayak SR (eds) Deep learning for sustainable agriculture. Elsevier, pp 143–168
Republic of Türkiye Ministry of Agriculture and Forestry (2023a) Fırat Alt Havzası, Dicle Alt Havzası Taşkın Yönetim Planı, Ankara. https://www.tarimorman.gov.tr/SYGM/Sayfalar/Detay.aspx?SayfaId=53#
Republic of Türkiye Ministry of Agriculture and Forestry (2023b) Fırat Alt Havzası, Fırat Alt Havzası Taşkın Yönetim Planı, Ankara. https://www.tarimorman.gov.tr/SYGM/Sayfalar/Detay.aspx?SayfaId=53#
Rezaie-Balf M, Kim S, Fallah H, Alaghmand S (2019) Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: application on the perennial rivers in Iran and South Korea. J Hydrol (Amst) 572:470–485. https://doi.org/10.1016/j.jhydrol.2019.03.046
Saltelli A (2002) Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun 145:280–297. https://doi.org/10.1016/S0010-4655(02)00280-1
Sameen MI, Pradhan B, Lee S (2019) Self-learning random forests model for mapping groundwater yield in data-scarce areas. Nat Resour Res 28:757–775. https://doi.org/10.1007/s11053-018-9416-1
Sarıgöl M, Katipoğlu OM (2023) Estimation of monthly evaporation values using gradient boosting machines and mode decomposition techniques in the Southeast Anatolia Project (GAP) area in Turkey. Acta Geophys. https://doi.org/10.1007/s11600-023-01067-8
Sezen C, Partal T (2020) Wavelet combined innovative trend analysis for precipitation data in the Euphrates-Tigris basin, Turkey. Hydrol Sci J 65:1909–1927. https://doi.org/10.1080/02626667.2020.1784422
Stephens JC, Stewart EH (1963) A comparison of procedures for computing evaporation and evapotranspiration. Publication 62:123–133
Tao H, Al-Bedyry NK, Khedher KM et al (2021) River water level prediction in coastal catchment using hybridized relevance vector machine model with improved grasshopper optimization. J Hydrol (Amst) 598:126477. https://doi.org/10.1016/j.jhydrol.2021.126477
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res: Atmos 106:7183–7192. https://doi.org/10.1029/2000JD900719
Tezel G, Buyukyildiz M (2016) Monthly evaporation forecasting using artificial neural networks and support vector machines. Theor Appl Climatol 124:69–80. https://doi.org/10.1007/s00704-015-1392-3
Thornthwaite CW (1948) An Approach toward a rational classification of climate. Geogr Rev 38:55. https://doi.org/10.2307/210739
Torgo L (2017) Regression trees. Encyclopedia of Machine Learning and Data Mining. Springer US, Boston, MA, pp 1080–1083
Trabert W (1896) Neue Beobachtungenûber Verdampfungsgeschwindigkeiten. Meteorol Z 13:261–263
Tyralis H, Papacharalampous G, Langousis A (2019) A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water (Basel) 11:910. https://doi.org/10.3390/w11050910
Valeh S, Motamedvairi B, Kiadaliri H, Ahmadi H (2021) Hydrological simulation of Ammameh basin by artificial neural network and SWAT models. Phys Chem Earth Parts A/B/C 123:103014. https://doi.org/10.1016/j.pce.2021.103014
Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194. https://doi.org/10.1080/02723646.1981.10642213
Wu Z, Huang N (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 01:1–41. https://doi.org/10.1142/S1793536909000047
Wu L, Huang G, Fan J et al (2020) Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction. Comput Electron Agric 168:105115. https://doi.org/10.1016/J.COMPAG.2019.105115
Xia Y (2020) Correlation and association analyses in microbiome study integrating multiomics in health and disease. In: Sun J (ed) Progress in Molecular Biology and Translational Science. pp 309–491. https://doi.org/10.1016/bs.pmbts.2020.04.003
Xie S, Wu W, Mooser S et al (2021) Artificial neural network based hybrid modeling approach for flood inundation modeling. J Hydrol (Amst) 592:125605. https://doi.org/10.1016/j.jhydrol.2020.125605
Yan Z, Wang S, Ma D et al (2019) Meteorological factors affecting Pan Evaporation in the Haihe River Basin, China. Water (Basel) 11:317. https://doi.org/10.3390/W11020317
Yenigün K, Gümüş V, Bulut H (2008) Trends in streamflow of the Euphrates basin, Turkey. Proc Institution Civil Eng - Water Manage 161:189–198. https://doi.org/10.1680/wama.2008.161.4.189
Zaras A, Passalis N, Tefas A (2022) Neural networks and backpropagation. In: Iosifidis A, Tefas A (eds) Deep learning for Robot Perception and Cognition. Elsevier, pp 17–34
Zhang X, Zhang Q, Zhang G et al (2018) A novel hybrid data-driven model for daily land surface temperature forecasting using long short-term memory neural network based on ensemble empirical mode decomposition. Int J Environ Res Public Health 15:1032. https://doi.org/10.3390/ijerph15051032
Zhou R, Zhang Y (2022) Reconstruction of missing spring discharge by using deep learning models with ensemble empirical mode decomposition of precipitation. Environ Sci Pollut Res 29:82451–82466. https://doi.org/10.1007/s11356-022-21597-w
Zounemat-Kermani M, Kisi O, Piri J, Mahdavi-Meymand A (2019) Assessment of artificial intelligence–based models and metaheuristic algorithms in modeling evaporation. J Hydrol Eng 24:04019033. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001835
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Sezen, C. Pan evaporation forecasting using empirical and ensemble empirical mode decomposition (EEMD) based data-driven models in the Euphrates sub-basin, Turkey. Earth Sci Inform 16, 3077–3095 (2023). https://doi.org/10.1007/s12145-023-01078-5
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DOI: https://doi.org/10.1007/s12145-023-01078-5