Reference evapotranspiration prediction using high-order response surface method

Article

Keshtegar, Behrooz, Abdullah, Shafika Sultan, Huang, Yuk Feng, Saggi, Mandeep Kaur, Khedher, Khaled Mohamed and Yaseen, Zaher Mundher. 2022. "Reference evapotranspiration prediction using high-order response surface method." Theoretical and Applied Climatology. 148 (1-2), pp. 849-867. https://doi.org/10.1007/s00704-022-03954-4
Article Title

Reference evapotranspiration prediction using high-order response surface method

ERA Journal ID1991
Article CategoryArticle
AuthorsKeshtegar, Behrooz, Abdullah, Shafika Sultan, Huang, Yuk Feng, Saggi, Mandeep Kaur, Khedher, Khaled Mohamed and Yaseen, Zaher Mundher
Journal TitleTheoretical and Applied Climatology
Journal Citation148 (1-2), pp. 849-867
Number of Pages19
Year2022
PublisherSpringer
Place of PublicationAustria
ISSN0177-798X
1434-4483
Digital Object Identifier (DOI)https://doi.org/10.1007/s00704-022-03954-4
Web Address (URL)https://link.springer.com/article/10.1007/s00704-022-03954-4
Abstract

The precision of reference evapotranspiration (ETo) predictions would vary, depending on the adopted empirical method and the availability of meteorological data. This study aims to enhance the prediction accuracy of ETo using the high-order response surface method (HO-RSM). Daily scale climatological information are used to build the predictive model including maximum temperature (Tmax), maximum humidity (Hmax), wind speed (WS), solar radiation (SR), and vapor pressure deficit (VPD), which are obtained from three observation stations in Burkina Faso, West Africa. Ten models corresponding to ten different input combination sets are evaluated for variability influence by comparing the predicted ETo with the observed ETo. The models presented a similar performance at both Gaoua and Boromo stations with the determination coefficient (R2) and root mean square error (RMSE) values ranging between 0.6831–0.9966 (0.0622–0.5065) and 0.7237–0.9948 (0.0722–0.4942), respectively. As for the Dori station, the models showed a lower performance with R2 (RMSE) values ranging between 0.2068 and 0.5229 (0.8292–1.0051), which may be due to the insufficient input variables or the requirement of higher order in RSM modeling for this station. Results also showed that the M10 model that includes all five input variables performed the best at three stations, with respect to the statistical performance. This is followed by the M7 model, which excluded the Hmax in the prediction, suggesting that Hmax has the least influence on the ETo prediction among all the input variables. The insignificant trend in selecting the optimum order of the RSM also showed that HO-RSM is case sensitive and hence precautions are required for generalizing model applications.

Keywordsevapotranspiration; surface method; wind speed; solar radiation
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Byline AffiliationsUniversity of Zabol, Iran
Duhok Polytechnic University, Iraq
University Tunku Abdul Rahman, Malaysia
Thapar Institute of Engineering and Technology, India
King Khalid University, Saudi Arabia
Mrezgua University Campus, Tunisia
School of Mathematics, Physics and Computing
South Ural State University, Russia
Asia University, Taiwan
Al-Ayen University, Iraq
MARA University of Technology, Malaysia
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Elmetwalli, Adel H., Mazrou, Yasser S. A., Tyler, Andrew N., Hunter, Peter D., Elsherbiny, Osama, Yaseen, Zaher Mundher and Elsayed, Salah. 2022. "Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt." Agriculture. 12 (3). https://doi.org/10.3390/agriculture12030332
Households’ perceptions and socio-economic determinants of climate change awareness: Evidence from Selangor Coast Malaysia
Ehsan, Sofia, Begum, Rawshan Ara, Maulud, Khairul Nizam Abdul and Yaseen, Zaher Mundher. 2022. "Households’ perceptions and socio-economic determinants of climate change awareness: Evidence from Selangor Coast Malaysia." Journal of Environmental Management. 316. https://doi.org/10.1016/j.jenvman.2022.115261
Surface water sodium (Na+) concentration prediction using hybrid weighted exponential regression model with gradient-based optimization
Ahmadianfar, Iman, Shirvani-Hosseini, Seyedehelham, Samadi-Koucheksaraee, Arvin and Yaseen, Zaher Mundher. 2022. "Surface water sodium (Na+) concentration prediction using hybrid weighted exponential regression model with gradient-based optimization." Environmental Science and Pollution Research. 29 (35), pp. 53456-53481. https://doi.org/10.1007/s11356-022-19300-0
An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction
Ahmadianfar, Iman, Shirvani-Hosseini, Seyedehelham, He, Jianxun He, Samadi-Koucheksaraee, Arvin and Yaseen, Zaher Mundher. 2022. "An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction." Scientific Reports. 12 (1). https://doi.org/10.1038/s41598-022-08875-w
Thermal and Hydraulic Performances of Carbon and Metallic Oxides-Based Nanomaterials
Afan, Haitham Abdulmohsin, Aldlemy, Mohammed Suleman, Ahmed, Ali M., Jawad, Ali H., Naser, Maryam H., Homod, Raad Z., Mussa, Zainab Haider, Abdulkadhim, Adnan Hashim, Scholz, Miklas and Yaseen, Zaher Mundher. 2022. "Thermal and Hydraulic Performances of Carbon and Metallic Oxides-Based Nanomaterials." Nanomaterials. 12 (9). https://doi.org/10.3390/nano12091545
Different TiO2 Phases (Degussa/Anatase) Modified Cross-Linked Chitosan Composite for the Removal of Reactive Red 4 Dye: Box–Behnken Design
Abdulhameed, Ahmed Saud, Jawad, Ali H., Vigneshwaran, Sivakumar, ALOthman, Zeid A. and Yaseen, Zaher Mundher Yaseen. 2022. "Different TiO2 Phases (Degussa/Anatase) Modified Cross-Linked Chitosan Composite for the Removal of Reactive Red 4 Dye: Box–Behnken Design." Journal of Polymers and the Environment. 30 (12), pp. 5084-5099. https://doi.org/10.1007/s10924-022-02568-1
Chitosan/Carbon-Doped TiO2 Composite for Adsorption of Two Anionic Dyes in Solution and Gaseous SO2 Capture: Experimental Modeling and Optimization
Abdulhameed, Ahmed Saud, Jawad, Ali H., Ridwan, Muhammad, Khadiran, Tumirah, Wilson, Lee D. and Yaseen, Zaher Mundher. 2022. "Chitosan/Carbon-Doped TiO2 Composite for Adsorption of Two Anionic Dyes in Solution and Gaseous SO2 Capture: Experimental Modeling and Optimization." Journal of Polymers and the Environment. 30 (11), pp. 4619-4636. https://doi.org/10.1007/s10924-022-02532-z
Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection
Malik, Anurag, Jamei, Mehdi, Ali, Mumtaz, Prasad, Ramendra, Karbasi, Masoud and Yaseen, Zaher Mundher. 2022. "Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection." Agricultural Water Management. 272. https://doi.org/10.1016/j.agwat.2022.107812
Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach
Jamei, Mehdi, Ali, Mumtaz, Karbasi, Masoud, Xiang, Yong, Ahmadianfar, Iman and Yaseen, Zaher Mundher. 2022. "Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach ." Applied Energy. 326, pp. 1-24. https://doi.org/10.1016/j.apenergy.2022.119925
Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer
Karbasi, Masoud, Jamei, Mehdi, Ali, Mumtaz, Malik, Anurag and Yaseen, Zaher Mundher. 2022. "Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer." Computers and Electronics in Agriculture. 198, pp. 1-17. https://doi.org/10.1016/j.compag.2022.107121
Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
Tao, Hai, Al-Khafaji, Zainab S., Qi, Chongchong, Zounemat-Kermani, Mohammad, Kisi, Ozgur, Tiyasha, Tiyasha, Chau, Kwok-Wing, Nourani, Vahid, Melesse, Assefa M., Elhakeem, Mohamed, Farooque, Aitazaz Ahsan, Nejadhashemi, A. Pouyan, Khedher, Khaled Mohamed, Alawi, Omer A., Deo, Ravinesh C., Shahid, Shamsuddin, Singh, Vijay P. and Yaseen, Zaher Mundher. 2021. "Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions." Engineering Applications of Computational Fluid Mechanics. 15 (1), pp. 1585-1612. https://doi.org/10.1080/19942060.2021.1984992
Streamflow prediction using an integrated methodology based on convolutional neural network and long short‑term memory networks
Ghimire, Sujan, Yaseen, Zaher Mundher, Farooque, Aitazaz A., Deo, Ravinesh C., Zhang, Ji and Tao, Xiaohui. 2021. "Streamflow prediction using an integrated methodology based on convolutional neural network and long short‑term memory networks." Scientific Reports. 11, pp. 1-26. https://doi.org/10.1038/s41598-021-96751-4
Modeling soil temperature using air temperature features in diverse climatic conditions with complementary machine learning models
Bayatvarkeshi, Maryam, Bhagat, Suraj Kumar, Mohammadi, Kourosh, Kisi, Ozgur, Farahani, M., Hasani, A., Deo, Ravinesh and Yaseen, Zaher Mundher. 2021. "Modeling soil temperature using air temperature features in diverse climatic conditions with complementary machine learning models." Computers and Electronics in Agriculture. 185. https://doi.org/10.1016/j.compag.2021.106158
Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology
Ali, Mumtaz, Prasad, Ramendra, Xiang, Yong, Khan, Mohsin, Farooque, Aitazaz Ahsan, Zong, Tianrui and Yaseen, Zaher Mundher. 2021. "Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology." Energy Reports. 7, pp. 6700-6717. https://doi.org/10.1016/j.egyr.2021.09.113
Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh
Yaseen, Zaher Mundher, Ali, Mumtaz, Sharafati, Ahmad, Al-Ansari, Nadhir and Shahid, Shamsuddin. 2021. "Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh." Scientific Reports. 11 (1), pp. 1-25. https://doi.org/10.1038/s41598-021-82977-9
Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach
Ali, Mumtaz, Deo, Ravinesh C., Xiang, Yong, Li, Ya and Yaseen, Zaher Mundher. 2020. "Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach." Hydrological Sciences Journal. 65 (16), pp. 2693-2708. https://doi.org/10.1080/02626667.2020.1808219
Hybrid multilayer perceptron-firefly optimizer algorithm for modelling photosynthetic active solar radiation for biofuel energy exploration
Goundar, Harshna, Yaseen, Zaher Mundher and Deo, Ravinesh. 2021. "Hybrid multilayer perceptron-firefly optimizer algorithm for modelling photosynthetic active solar radiation for biofuel energy exploration." Deo, Ravinesh, Samui, Pijush and Roy, Sanjiban Sekhar (ed.) Predictive modelling for energy management and power systems engineering. Amsterdam, Netherlands. Elsevier. pp. 191-232
Bayesian Markov Chain Monte Carlo-based copulas: factoring the role of large-scale climate indices in monthly flood prediction
Nguyen-Huy, Thong, Deo, Ravinesh C., Yaseen, Zaher Mundher, Mushtaq, Shahbaz and Prasad, Ramendra. 2021. "Bayesian Markov Chain Monte Carlo-based copulas: factoring the role of large-scale climate indices in monthly flood prediction." Deo, Ravinesh C., Samui, Pijush, Kisi, Ozgur and Yaseen, Zaher Mundher (ed.) Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation. Singapore. Springer. pp. 29-47
Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation
Deo, Ravinesh C., Samui, Pijush, Kisi, Ozgur and Yaseen, Zaher Mundher. 2021. Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation. Singapore. Springer.
Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction
Sharafati, Ahmad, Haghbin, Masoud, Aldlemy, Mohammed Suleman, Mussa, Mohamed H., Al Zand, Ahmed W., Ali, Mumtaz, Bhagat, Suraj Kumar, Al-Ansari, Nadhir and Yaseen, Zaher Mundher. 2020. "Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction." Applied Sciences. 10 (11), pp. 1-25. https://doi.org/10.3390/app10113811
Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments
Salih, Sinan Q., Sharafati, Ahmad, Ebtehaj, Isa, Sanikhani, Hadi, Siddique, Ridwan, Deo, Ravinesh C., Bonakdari, Hossein, Shahid, Shamsuddin and Yaseen, Zaher Mundher. 2020. "Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments." Hydrological Sciences Journal. 65 (7), pp. 1145-1157. https://doi.org/10.1080/02626667.2020.1734813
Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model
Tao, Hai, Sharafati, Ahmad, Mohammed, Achite, Salih, Sinan Q., Deo, Ravinesh C., Al-Ansari, Nadhir and Yaseen, Zaher Mundher. 2020. "Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model." IEEE Access. 8, pp. 12026-12042. https://doi.org/10.1109/ACCESS.2020.2965303
Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts
Ali, Mumtaz, Prasad, Ramendra, Xiang, Yong and Yaseen, Z.. 2020. "Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts." Journal of Hydrology. 584, pp. 1-15. https://doi.org/10.1016/j.jhydrol.2020.124647
Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models
Yaseen, Zaher Mundher, Al-Juboori, Al-Juboori, Beyaztasc, Ufuk, Al-Ansari, Nadhir, Chau, Kwok-Wing, Qi, Chongchong, Ali, Mumtaz, Salih, Sinan Q. and Shahid, Shamsuddin. 2020. "Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models." Engineering Applications of Computational Fluid Mechanics. 14 (1), pp. 70-89. https://doi.org/10.1080/19942060.2019.1680576
Modern artificial intelligence model development for undergraduate student performance prediction: an investigation on engineering mathematics courses
Deo, Ravinesh C., Yaseen, Zaher Mundher, Al-Ansari, Nadhir, Nguyen-Huy, Thong, Langlands, Trevor and Galligan, Linda. 2020. "Modern artificial intelligence model development for undergraduate student performance prediction: an investigation on engineering mathematics courses." IEEE Access. 8, pp. 136697-136724. https://doi.org/10.1109/ACCESS.2020.3010938
Designing a new data intelligence model for global solar radiation prediction: application of multivariate modeling scheme
Tao, Hai, Ebtehaj, Isa, Bonakdari, Hossein, Heddam, Salim, Voyant, Cyril, Al-Ansari, Nadhir, Deo, Ravinesh and Yaseen, Zaheer Mundher. 2019. "Designing a new data intelligence model for global solar radiation prediction: application of multivariate modeling scheme." Energies. 12 (7), pp. 1-24. https://doi.org/10.3390/en12071365
Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models
Kisi, Ozgur, Choubin, Bahram, Deo, Ravinesh C. and Yaseen, Zaheer Mundher. 2019. "Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models." Hydrological Sciences Journal. 64 (10), pp. 1240-1252. https://doi.org/10.1080/02626667.2019.1632460
Long-term modelling of wind speeds using six different heuristic artificial intelligence approaches
Maroufpoor, Saman, Sanikhani, Hadi, Kisi, Ozgur, Deo, Ravinesh C. and Yaseen, Zaher Mundher. 2019. "Long-term modelling of wind speeds using six different heuristic artificial intelligence approaches." International Journal of Climatology. 39 (8), pp. 3543-3557. https://doi.org/10.1002/joc.6037
An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction
Yaseen, Zaher Mundher, Sulaiman, Sadeq Oleiwi, Deo, Ravinesh C. and Chau, Kwok-Wing. 2019. "An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction." Journal of Hydrology. 569, pp. 387-408. https://doi.org/10.1016/j.jhydrol.2018.11.069
Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model
Hou, Muzhou, Zhang, Tianle, Weng, Futian, Ali, Mumtaz, Al-Ansari, Nadhir and Yaseen, Zaher Mundher. 2018. "Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model." Energies. 11 (12), pp. 1-19. https://doi.org/10.3390/en11123415
Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: a new approach
Yaseen, Zaheer Munder, Tran, Minh Tung, Kim, Sungwon, Bakhshpoori, Taha and Deo, Ravinesh C.. 2018. "Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: a new approach." Engineering Structures. 177, pp. 244-255. https://doi.org/10.1016/j.engstruct.2018.09.074
Hybrid data intelligent models and applications for water level prediction
Yaseen, Zaher Mundher, Deo, Ravinesh C., Ebtehaj, Isa and Bonakdari, Hossein. 2018. "Hybrid data intelligent models and applications for water level prediction." Kim, Dookie, Roy, Sanjiban Sekhar, Länsivaara, Tim, Deo, Ravinesh C. and Samui, Pijush (ed.) Handbook of research on predictive modeling and optimization methods in science and engineering. Hershey, United States. IGI Global. pp. 121-139
Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors
Sanikhani, Hadi, Deo, Ravinesh C., Samui, Pijush, Kisi, Ozgur, Mert, Chian, Mirabbasi, Rasoul, Gavili, Siavash and Yaseen, Zaher Mundher. 2018. "Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors." Computers and Electronics in Agriculture. 152, pp. 242-260. https://doi.org/10.1016/j.compag.2018.07.008
Non-tuned data intelligent model for soil temperature estimation: a new approach
Sanikhani, Hadi, Deo, Ravinesh C., Yaseen, Zaher Mundheer, Eray, Okan and Kisi, Ozgur. 2018. "Non-tuned data intelligent model for soil temperature estimation: a new approach." Geoderma. 330, pp. 52-64. https://doi.org/10.1016/j.geoderma.2018.05.030
Application of the hybrid artificial neural network coupled with rolling mechanism and grey model algorithms for streamflow forecasting over multiple time horizons
Yaseen, Zaher Mundher, Fu, Minglei, Wang, Chen, Mohtar, Wan Hanna Melini Wan, Deo, Ravinesh C. and El-Shafie, Ahmed. 2018. "Application of the hybrid artificial neural network coupled with rolling mechanism and grey model algorithms for streamflow forecasting over multiple time horizons." Water Resources Management. 32 (5), pp. 1883-1899. https://doi.org/10.1007/s11269-018-1909-5
Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey
Ghorbani, Mohammad Ali, Deo, Ravinesh C., Karimi, Vahid, Yaseen, Zaher Mundher and Terz, Ozlem. 2018. "Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey." Stochastic Environmental Research and Risk Assessment. 32 (6), pp. 1683-1697. https://doi.org/10.1007/s00477-017-1474-0
Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran
Ghorbani, M. A., Deo, Ravinesh C., Yaseen, Zaher Mundher, Kashani, Mahsa H. and Mohammadi, Babak. 2018. "Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran." Theoretical and Applied Climatology. 133 (3-4), pp. 1119-1131. https://doi.org/10.1007/s00704-017-2244-0
Predicting compressive strength of lightweight foamed concrete using extreme learning machine model
Yaseen, Zaher Mundher, Deo, Ravinesh C., Hilal, Ameer, Abd, Abbas M., Bueno, Laura Cornejo, Salcedo-sanz, Sancho and Nehdi, Moncef L.. 2018. "Predicting compressive strength of lightweight foamed concrete using extreme learning machine model." Advances in Engineering Software. 115, pp. 112-125. https://doi.org/10.1016/j.advengsoft.2017.09.004
Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
Yaseen, Zaher Mundher, Ebtehaj, Isa, Bonakdari, Hossein, Deo, Ravinesh C., Mehr, Ali Danandeh, Mohtar, Wan Hanna Melini Wan, Diop, Lamine, El-Shafie, Ahmed and Singh, Vijay P.. 2017. "Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model." Journal of Hydrology. 554, pp. 263-276. https://doi.org/10.1016/j.jhydrol.2017.09.007
Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA
Yaseen, Zaher Mundher, Ghareb, Mazen Ismaeel, Ebtehaj, Isa, Bonakdari, Hossein, Siddique, Ridwan, Heddam, Sali, Yusif, Ali A. and Deo, Ravinesh. 2018. "Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA." Water Resources Management. 32 (1), pp. 105-122. https://doi.org/10.1007/s11269-017-1797-0
Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq
Yaseen, Zaher Mundher, Jaafar, Othman, Deo, Ravinesh C., Kisi, Ozgur, Adamowski, Jan, Quilty, John and El-Shafie, Ahmed. 2016. "Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq." Journal of Hydrology. 542, pp. 603-614. https://doi.org/10.1016/j.jhydrol.2016.09.035