Abstract
The present study evaluates the capability of a novel optimization method in modeling daily crop reference evapotranspiration (ETo), a critical issue in water resource management. A hybrid predictive model based on the artificial neural network (ANN) algorithm that is embedded within the COOT method (COOT bird natural life model-artificial neural network (COOT-ANN)) is developed and evaluated for its suitability for the prediction of daily ETo at seven meteorological stations in different states of Australia. Accordingly, a daily statistical period of 12 years (01-01-2010 to 31-12-2021) for climatic data of maximum temperature, minimum temperature, and ETo were collected. The results are evaluated using six performance criteria metrics: correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), RMSE-observation standard deviation ratio (RSR), Scatter Index (SI), and mean absolute error (MAE) along with the Taylor diagrams. The performance of the COOT-ANN model was compared with those of the conventional ANN model. The results showed that the COOT-ANN hybrid model outperforms the ANN model at all seven stations by 0.803%, 4.127%, 3.359%, 4.072%, 4.148%, and 3.665% based on the average values of the R, RMSE, NSE, RSR, SI, and MAE criteria, respectively. So, this study provides an innovative method for prediction in agricultural and water resource studies.
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets, analyzed during the current study, were compiled and supplied from the Australian Meteorological Agency (BOM) (http://www.bom.gov.au). They are available from the corresponding author on reasonable request.
References
Abrishami N, Sepaskhah AR, Shahrokhnia MH (2019) Estimating wheat and maize daily evapotranspiration using artificial neural network. Theor Appl Climatol 135:945–958. https://doi.org/10.1007/s00704-018-2418-4
Achite M, Jehanzaib M, Sattari MT, Toubal AK, Elshaboury N, Wałęga A, Krakauer N, Yoo JY, Kim TW (2022) Modern techniques to modeling reference evapotranspiration in a semiarid area based on ANN and GEP models. Water 14:1210. https://doi.org/10.3390/w14081210
Adnan RM, Heddam S, Yaseen ZM, Shahid S, Kisi O, Li B (2021) Prediction of potential evapotranspiration using temperature-based heuristic approaches. Sustainability 13:297. https://doi.org/10.3390/su13010297
Ahmadi F, Mehdizadeh S, Mohammadi B, Pham QB, Doan TNC, Vo ND (2021) Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation. Agric Water Manag 244:106622. https://doi.org/10.1016/j.agwat.2020.106622
Ahmed AAM, Deo RC, Feng Q, Ghahramani A, Raj N, Yin Z, Yang L (2022) Hybrid deep learning method for a week-ahead evapotranspiration forecasting. Stoch Environ Res Risk Assess 36:831–849. https://doi.org/10.1007/s00477-021-02078-x
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements. In: FAO Irrigation and Drainage Paper 56. Food and Agriculture Organization of the United Nations, Rome, Italy
Antonopoulos VZ, Antonopoulos AV (2017) Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables. Comput Electron Agric 132:86–96
Chen X, Li FW, Wang YX, Feng P, Yang RZ (2019a) Evolution properties between meteorological, agricultural and hydrological droughts and their related driving factors in the Luanhe River basin, China. Hydrol Res 50:1096–1119. https://doi.org/10.2166/nh.2019.141
Chen Z, Yang X, Liu X (2019b) RBFNN-based non-singular fast terminal sliding mode control for robotic manipulators including actuator dynamics. Neurocomputing 362:72–82. https://doi.org/10.1016/j.neucom.2019.06.083
Chen Z, Zhu Z, Jiang H, Sun S (2020) Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods. J Hydrol 591. https://doi.org/10.1016/j.jhydrol.2020.125286
Dias SHB, Filgueiras R, Filho EIF, Arcanjo GS, Silva GH, Mantovani EC, Cunha FF (2021) Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing. PLoS ONE 16(2):e0245834. https://doi.org/10.1371/journal.pone.0245834
Djaman K, O’Neill M, Diop L, Bodian A, Allen S, Koudahe K, Lombard K (2019) Evaluation of the Penman-Monteith and other 34 reference evapotranspiration equations under limited data in a semiarid dry climate. Theor Appl Climatol 137(1):729–743. https://doi.org/10.1007/s00704-018-2624-0
Douna V, Barraza V, Grings F, Huete A, Coupe NR, Beringer J (2021) Towards a remote sensing data based evapotranspiration estimation in Northern Australia using a simple random forest approach. J Arid Environ 191:104513. https://doi.org/10.1016/j.jaridenv.2021.104513
Elbeltagi A, Kushwaha NL, Rajput J et al (2022) Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions. Stoch Environ Res Risk Assess 36:3311–3334. https://doi.org/10.1007/s00477-022-02196-0
Exner-Kittridge MG, Rains MC (2010) Case study on the accuracy and cost/effectiveness in simulating reference evapotranspiration in West-Central Florida. J Hydrol Eng 15(9):696–703. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000239
Falamarzi Y, Palizdan N, Huang YF, Lee TS (2014) Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs). Agric Water Manage 140:26–36. https://doi.org/10.1016/j.agwat.2014.03.014
Fan J, Yue W, Wu L, Zhang F, Cai H, Wang X, Lu X, Xiang Y (2018) Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agric For Meteorol 263:225–241. https://doi.org/10.1016/j.agrformet.2018.08.019
Farias DBS, Althoff D, Rodrigues LN, Filgueiras R (2020) Performance evaluation of numerical and machine learning methods in estimating reference evapotranspiration in a Brazilian agricultural frontier. Theor Appl Climatol 142:1481–1492. https://doi.org/10.1007/s00704-020-03380-4
Feng Y, Gong D, Mei X, Cui N (2017) Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau. Hydrol Res 48(4):1156–1168. https://doi.org/10.2166/nh.2016.099
Ferreira LB, Cunha FFD, Oliveira RAD, Filho EIF (2019) Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM—A new approach. J Hydrol 572:556–570. https://doi.org/10.1016/j.jhydrol.2019.03.028
Gao L, Gong D, Cui N, Lv M, Feng Y (2021) Evaluation of bio-inspired optimization algorithms hybrid with artificial neural network for reference crop evapotranspiration estimation. Comput Electron Agric 190:106466. https://doi.org/10.1016/j.compag.2021.106466
Gocić M, Amiri MA (2021) Reference evapotranspiration prediction using neural networks and optimum time lags. Water Resour Manage 35:1913–1926. https://doi.org/10.1007/s11269-021-02820-8
Hashemi M, Sepaskhah AR (2020) Evaluation of artificial neural network and Penman–Monteith equation for the prediction of barley standard evapotranspiration in a semi-arid region. Theor Appl Climatol 139:275–285. https://doi.org/10.1007/s00704-019-02966-x
Huang G, Wu L, Ma X, Zhang W, Fan J, Yu X, Zeng W, Zhou H (2019) Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions. J Hydrol 574:1029–1041. https://doi.org/10.1016/j.jhydrol.2019.04.085
Kaya YZ, Zelenakova M, Üneş F, Demirci M, Hlavata H, Mesaros P (2021) Estimation of daily evapotranspiration in Košice City (Slovakia) using several soft computing techniques. Theor Appl Climatol 144:287–298. https://doi.org/10.1007/s00704-021-03525-z
Keshtegar B, Abdullah SS, Huang YF et al (2022) Reference evapotranspiration prediction using high-order response surface method. Theor Appl Climatol 148:849–867. https://doi.org/10.1007/s00704-022-03954-4
Kisi O (2005) Suspended sediment estimation using neurofuzzy and neural network approaches. Hydrol Sci J 50(4):683–696
Kisi O, Keshtegar B, Zounemat-Kermani M, Heddam S, Trung NT (2021) Modeling reference evapotranspiration using a novel regression-based method: radial basis M5 model tree. Theor Appl Climatol 145:639–659. https://doi.org/10.1007/s00704-021-03645-6
Koudahe K, Djaman K, Adewumi JK (2018) Evaluation of the Penman–Monteith reference evapotranspiration under limited data and its sensitivity to key climatic variables under humid and semiarid conditions. Model Earth Syst Environ 4(3):1239–1257. https://doi.org/10.1007/s40808-018-0497-y
Majhi B, Naidu D (2021) Differential evolution based radial basis function neural network model for reference evapotranspiration estimation. SN Appl Sci 3(1):1–19. https://doi.org/10.1007/s42452-020-04069-z
Majumdar P, Bhattacharya D, Mitra S (2023) Prediction of evapotranspiration and soil moisture in different rice growth stages through improved salp swarm based feature optimization and ensembled machine learning algorithm. Theor Appl Climatol. https://doi.org/10.1007/s00704-023-04414-3
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. J Hydrol 588. https://doi.org/10.1016/j.jhydrol.2020.125060
Mawgoud AH, Fathy A, Kamel S (2022) An effective hybrid approach based on arithmetic optimization algorithm and sine cosine algorithm for integrating battery energy storage system into distribution networks. J Energy Storage 49:104154 https://doi.org/10.1016/j.est.2022.104154
Mirzania E, Vishwakarma DK, Bui QAT et al (2023) A novel hybrid AIG-SVR model for estimating daily reference evapotranspiration. Arab J Geosci 16:301. https://doi.org/10.1007/s12517-023-11387-0
Mohammadi B, Linh NTT, Pham QB, Ahmed AN, Vojteková J, Guan Y, Abba SI, El-Shafie A (2020) Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series. Hydrol Sci J 65:1738–1751. https://doi.org/10.1080/02626667.2020.1758703
Mokari E, DuBois D, Samani Z, Mohebzadeh H, Djaman K (2021) Estimation of daily reference evapotranspiration with limited climatic data using machine learning approaches across different climate zones in New Mexico. Theor Appl Clim 147:575–587. https://doi.org/10.1007/s00704-021-03855-y
Mostafa RR, Hussien AG, Khan MA, Kadry S, Hashim FA (2022) Enhanced COOT optimization algorithm for Dimensionality Reduction. 2022 Fifth Int Conf Women Data Sci Prince Sultan Univ (WiDS PSU):43–48. https://doi.org/10.1109/WiDS-PSU54548.2022.00020
Muhammad MKI, Shahid S, Ismail T, Harun S, Kisi O, Yaseen ZM (2021) The development of evolutionary computing model for simulating reference evapotranspiration over Peninsular Malaysia. Theor Appl Climatol 144:1419–1434. https://doi.org/10.1007/s00704-021-03606-z
Naruei I, Keynia F (2021) A new optimization method based on COOT Bird Natural Life Model. Expert Syst Appl 183:115352. https://doi.org/10.1016/j.eswa.2021.115352
Nawandar NK, Cheggoju N, Satpute V (2021) ANN-based model to predict reference evapotranspiration for irrigation estimation. In: Gunjan VK, Zurada JM (eds) Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Advances in Intelligent Systems and Computing, vol 1245. Springer, Singapore https://doi.org/10.1007/978-981-15-7234-0_63
Niaghi AR, Hassanijalilian O, Shiri J (2021) Estimation of reference evapotranspiration using spatial and temporal machine learning approaches. Hydrology 8:25. https://doi.org/10.3390/hydrology8010025
Nourani V (2017) An emotional ANN (EANN) approach to modeling rainfall-runoff process. J Hydrol 544:267–277. https://doi.org/10.1016/j.jhydrol.2016.11.033
Nourani V, Elkiran G, Abdullahi J (2019) Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements. J Hydrol 577:123958. https://doi.org/10.1016/j.jhydrol.2019.123958
Perera KC, Western AW, Nawarathna B, George B (2014) Forecasting daily reference evapotranspiration for Australia using numerical weather prediction outputs. Agric For Meteorol 194:50–63. https://doi.org/10.1016/j.agrformet.2014.03.014
Pour SH, Wahab AKA, Shahid S, Wang X (2019) Spatial pattern of the unidirectional trends in thermal bioclimatic indicators in Iran. Sustainability 11:2287. https://doi.org/10.3390/su11082287
Rajaee T, Shahabi A (2015) Evaluation of wavelet-GEP and wavelet-ANN hybrid models for prediction of total nitrogen concentration in coastal marine waters. Arab J Geosci 9:176
Sabzevari Y, Eslamian S (2022) Predicting the effect of temperature changes on reference evapotranspiration by means of time series modeling (case study: Khorramabad Basin). Irrig Sci Eng 45(2):125–138 https://doi.org/10.22055/jise.2022.40355.2022
Salman SA, Shahid S, Ismail T, Chung ES, Al-Abadi AM (2017) Longterm trends in daily temperature extremes in Iraq. Atmos Res 198:97–107. https://doi.org/10.1016/j.atmosres.2017.08.011
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. J Water Clim Change 11(1):217–240. https://doi.org/10.2166/wcc.2018.003
Sharafi S, Ghaleni MM (2021) Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran. Theor Appl Climatol 143(3):1409–1423. https://doi.org/10.1007/s00704-020-03473-0
Shiri J (2017) Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran. Agric Water Manag 188:101–114. https://doi.org/10.1016/j.agwat.2017.04.009
Silva CRD, Barbosa LA, Finzi RR, Riberio BT, Dias NDS (2020) Accuracy of air temperature forecasts and its use for prediction of the reference evapotranspiration. Biosci J 36:17–22. https://doi.org/10.14393/BJ-v36n1a2020-42188
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
Üneş F, Kaya YZ, Mamak M (2020) Daily reference evapotranspiration prediction based on climatic conditions applying different data mining techniques and empirical equations. Theor Appl Climatol 141(1–2):763–773. https://doi.org/10.1007/s00704-020-03225-0
Valiantzas JD (2015) Simplified limited data Penman’s ET0 formulas adapted for humid locations. J Hydrol 524:701–707. https://doi.org/10.1016/j.jhydrol.2015.03.021
Yamaç SS, Todorovic M (2020) Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data. Agric Water Manag 228:105875. https://doi.org/10.1016/j.agwat.2019.105875
Yang Q, Wang JQ, Hakala K (2022) Calibrating anomalies improves forecasting of daily reference crop evapotranspiration. J Hydrol 610. https://doi.org/10.1016/j.jhydrol.2022.128009
Yu H, Wen X, Li B, Yang Z, Wu M, Ma Y (2020) Uncertainty analysis of artificial intelligence modeling daily reference evapotranspiration in the northwest end of China. Comput Electron Agric 176:105653. https://doi.org/10.1016/j.compag.2020.105653
Zhao T, Wang QJ, Schepen A, Griffiths M (2019) Ensemble forecasting of monthly and seasonal reference crop evapotranspiration based on global climate model outputs. Agric Meteorol 264:114–124. https://doi.org/10.1016/j.agrformet.2018.10.001
Author information
Authors and Affiliations
Contributions
Ehsan Mirzania and Mohsen Saroughi: conceived the problem, data collection, and designed the analysis. Mahsa Hasanpour Kashani and Ehsan Mirzania: prepared draft and graphical design. Osama Ragab Ibrahim and Mohsen Saroughi: contributed data and analysis tools and writing review and editing. Mohsen Saroughi and Ehsan Mirzania: Coding and Runing. Golmar Golmohammadi: supervision. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mirzania, E., Kashani, M.H., Golmohammadi, G. et al. Hybrid COOT-ANN: a novel optimization algorithm for prediction of daily crop reference evapotranspiration in Australia. Theor Appl Climatol 154, 201–218 (2023). https://doi.org/10.1007/s00704-023-04552-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-023-04552-8