Assessment of Artificial Intelligence–Based Models and Metaheuristic Algorithms in Modeling Evaporation
This article has a reply.
VIEW THE REPLYThis article has a reply.
VIEW THE REPLYPublication: Journal of Hydrologic Engineering
Volume 24, Issue 10
Abstract
Evaporation () has a vital importance for the management and development of water resources projects. In this study two scenarios are considered in prediction of monthly pan evaporation. The first scenario challenges the ability of three artificial intelligence–based models [neural network autoregressive with exogenous input (NNARX), gene expression programming (GEP), and adaptive neuro-fuzzy inference system (ANFIS)]. The second scenario investigates the capability of five different metaheuristic algorithms [particle swarm optimization (PSO), firefly algorithm (FA), artificial bee colony (ABC), continuous ant colony optimization (CACO), and genetic algorithm (GA)] integrated with the ANFIS model in modeling. Meteorological factors (monthly air temperature, solar radiation, relative humidity, and wind speed data) of two stations in Turkey were used as inputs to the models. Various statistic measures [root-mean-square error (RMSE), mean absolute error (MAE), and determination coefficient ()] and diagnostic analysis (Taylor diagram) were deployed to evaluate and compare the performance of the models. The results of the first scenario show that the ANFIS model gave better performance in Gaziantep Station, whereas the NNARX model performed better in estimating values in Adiyaman Station. In the second scenario, it was observed that the PSO and GA algorithms performed better in comparison to the other algorithms in Gaziantep and Adiyaman stations, respectively. The non-parametric Kruskal–Wallis test denoted that there is a significant difference (alpha of 0.05) between the observed versus predicted amounts of monthly for the NNARX, ANFIS, and GEP. However, there is no sign of significant difference in predicting monthly between the applied metaheuristic algorithms.
Get full access to this article
View all available purchase options and get full access to this article.
References
Agarwal, V., and S. Bhanot. 2017. “Radial basis function neural network-based face recognition using firefly algorithm.” Neural Comput. Appl. 30 (8): 2643–2660. https://doi.org/10.1007/s00521-017-2874-2.
Aytek, A. 2009. “Co-active neurofuzzy inference system for evapotranspiration modeling.” Soft Comput. 13 (7): 691.
Azamathulla, H. M., A. A. Ghani, C. S. Leow, C. K. Chang, and N. A. Zakaria. 2011. “Gene-expression programming for the development of a stage-discharge curve of the Pahang River.” Water Resour. Manage. 25 (11): 2901–2916. https://doi.org/10.1007/s11269-011-9845-7.
Besdok, E. 2004. “A new method for impulsive noise suppression from highly distorted images by using ANFIS.” Eng. Appl. Artif. Intell. 17 (5): 519–527. https://doi.org/10.1016/j.engappai.2004.03.009.
Bruton, J. M., R. W. McClendon, and G. Hoogenboom. 2000. “Estimating daily pan evaporation with artificial neural networks.” Trans. ASAE 43 (2): 491–496. https://doi.org/10.13031/2013.2730.
De Martonne, E. 1926. Une nouvelle fonction climatologique: L’indice d’aridité: La Meteorologie, 449–458. Paris: Gauthier-Villars.
Deswal, S., and M. Pal. 2008. “Artificial neural network based modeling of evaporation losses in reservoirs.” Int. J. Math. Phys. Eng. Sci. 2 (4): 177–181.
Doorenbos, J., and W. O. Pruitt. 1977. Guidelines for predicting crop water requirements: Irrigation and drainage paper no. 24. 2nd ed. Rome: United Nations Food and Agriculture Organization.
Eslamian, S. S., S. A. Gohari, M. Biabanaki, and R. Malekian. 2008. “Estimation of monthly pan evaporation using artificial neural networks and support vector machines.” J. Appl. Sci. 8 (19): 3497–3502. https://doi.org/10.3923/jas.2008.3497.3502.
Falamarzi, Y., N. Palizdan, Y. F. Huang, and T. S. Lee. 2014. “Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs).” Agric. Water Manage. 140 (Jul): 26–36. https://doi.org/10.1016/j.agwat.2014.03.014.
Fallah-Mehdipour, E., O. Bozorg Haddad, H. Orouji, and M. A. Marino. 2013. “Application of genetic programming in stage hydrograph routing of open channels.” Water Resour. Manage. 27 (9): 3261–3272. https://doi.org/10.1007/s11269-013-0345-9.
Ferreira, C. 2001. “Gene expression programming: A new adaptive algorithm for solving problems.” Complex Syst. 13 (2): 87–129.
Gavili, S., H. Sanikhani, O. Kisi, and M. H. Mahmoudi. 2018. “Evaluation of several soft computing methods in monthly evapotranspiration modeling.” Meteorol. Appl. 25 (1): 128–138. https://doi.org/10.1002/met.1676.
Goyal, K. M., B. Bharti, J. Quilty, J. Adamowski, and A. Pandey. 2014. “Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS.” Expert Syst. Appl. 41 (11): 5267–5276. https://doi.org/10.1016/j.eswa.2014.02.047.
Holland, J. H. 1992. “Genetic algorithms.” Sci. Am. 267 (1): 66–72. https://doi.org/10.1038/scientificamerican0792-66.
Irmak, S., R. G. Allen, and E. B. Whitty. 2003. “Daily grass and alfalfa reference evapotranspiration estimates and alfalfa-to-grass evapotranspiration ratios in Florida.” J. Irrig. Drain. Eng. 129 (5): 360–370. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:5(360).
Jang, J. S. R. 1993. “ANFIS: Adaptive-network-based fuzzy inference systems.” IEEE Trans. Syst. Man Cybern. 23 (3): 665–685. https://doi.org/10.1109/21.256541.
Jang, J. S. R., and N. Gulley. 1995. The fuzzy logic toolbox for use with MATLAB. New York: Mathworks.
Kaboosi, K. 2012. “The investigation of error of pan evaporation data, estimation of pan evaporation coefficient by pan data and its comparison with empirical equations.” Int. J. Agric. Crop Sci. 4 (19): 1458–1465.
Karaboga, D. 2005. An idea based on honey bee swarm for numerical optimization. Kayseri, Turkey: Erciyes Univ.
Karaboga, D., B. Gorkemli, C. Ozturk, and N. Karaboga. 2014. “A comprehensive survey: Artificial bee colony (ABC) algorithm and applications.” Artif. Intell. Rev. 42 (1): 21–57. https://doi.org/10.1007/s10462-012-9328-0.
Karterakis, S. M., G. P. Karatzas, I. K. Nikolos, and M. P. Papadopoulou. 2007. “Application of linear programming and differential evolutionary optimization methodologies for the solution of coastal subsurface water management problems subject to environmental criteria.” J. Hydrol. 342 (3–4): 270–282. https://doi.org/10.1016/j.jhydrol.2007.05.027.
Karunanithi, N., W. J. Grenney, D. Whitley, and K. Bovee. 1994. “Neural networks for river flow prediction.” J. Comput. Civ. Eng. 8 (2): 201–220. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(201).
Kennedy, J., and R. Eberhart. 1995. “Particle swarm optimization (PSO).” In Proc. IEEE Int. Conf. on Neural Networks, 1942–1948. New York: IEEE.
Keshtegar, B., O. Kisi, H. Ghohani Arab, and M. Zounemat-Kermani. 2018. “Subset modeling basis ANFIS for prediction of the reference evapotranspiration.” Water Resour. Manage. 32 (3): 1101–1116. https://doi.org/10.1007/s11269-017-1857-5.
Keskin, M. E., and O. Terzi. 2006. “Artificial neural network models of daily pan evaporation.” J. Hydrol. Eng. 11 (1): 65–70. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:1(65).
Keskin, M. E., O. Terzi, and D. Taylan. 2004. “Fuzzy logic model approaches to daily pan evaporation estimation in Western Turkey.” Hydrol. Sci. J. 49 (6): 1001–1010. https://doi.org/10.1623/hysj.49.6.1001.55718.
Kim, S., V. P. Singh, and Y. Seo. 2014. “Evaluation of pan evaporation modeling with two different neural networks and weather station data.” Theor. Appl. Climatol. 117 (1–2): 1–13. https://doi.org/10.1007/s00704-013-0985-y.
Kisi, O. 2006. “Daily pan evaporation modeling using a neuro-fuzzy computing technique.” J. Hydrol. 329 (3–4): 636–646. https://doi.org/10.1016/j.jhydrol.2006.03.015.
Kisi, O. 2009. “Modeling monthly evaporation using two different neural computing techniques.” Irrig. Sci. 27 (5): 417–430. https://doi.org/10.1007/s00271-009-0158-z.
Kisi, O. 2013. “Evolutionary neural networks for monthly pan evaporation modeling.” J. Hydrol. 498 (19): 36–45. https://doi.org/10.1016/j.jhydrol.2013.06.011.
Kisi, O. 2015. “An innovative method for trend analysis of monthly pan evaporations.” J. Hydrol. 527 (Aug): 1123–1129. https://doi.org/10.1016/j.jhydrol.2015.06.009.
Kisi, O., T. Haktanir, M. Ardiclioglu, O. Ozturk, E. Yalcin, and S. Uludag. 2009. “Adaptive neuro-fuzzy computing technique for suspended sediment estimation.” Adv. Eng. Software 40 (6): 438–444. https://doi.org/10.1016/j.advengsoft.2008.06.004.
Kisi, O., and O. Ozturk. 2007. “Adaptive neurofuzzy computing technique for evapotranspiration estimation.” J. Irrig. Drain. Eng. 133 (4): 368–379. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(368).
Kisi, O., and M. Zounemat-Kermani. 2014. “Comparison of two different adaptive neuro fuzzy inference systems in modeling daily reference evapotranspiration.” Water Resour. Manage. 28 (9): 2655–2675. https://doi.org/10.1007/s11269-014-0632-0.
Koza, J. R. 1992. Genetic programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT.
Kumar, M., N. S. Raghuwanshi, R. Singh, W. W. Wallender, and W. O. Pruitt. 2002. “Estimating evapotranspiration using artificial neural network.” J. Irrig. Drain. Eng. 128 (4): 224–233. https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224).
Lin, T. N., B. G. Horne, P. Tino, and C. L. Giles. 1996. “Learning long-term dependencies in NARX recurrent neural networks.” IEEE Trans. Neural Networks 7 (6): 1329–1338. https://doi.org/10.1109/72.548162.
Mahdavi-Meymand, A., M. Scholz, and M. Zounemat-Kermani. 2019. “Challenging soft computing optimization approaches in modeling complex hydraulic phenomenon of aeration process.” ISH J. Hydraul. Eng. 1–12. https://doi.org/10.1080/09715010.2019.1574619.
Moghaddamnia, A., M. Ghafari Gousheh, J. Piri, S. Amin, and D. Han. 2009. “Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques.” Adv. Water Resour. 32 (1): 88–97. https://doi.org/10.1016/j.advwatres.2008.10.005.
Pisoni, E., M. Farina, C. Carnevale, and L. Piroddi. 2009. “Forecasting peak air pollution levels using NARX models.” Eng. Appl. Artif. Intell. 22 (4–5): 593–602. https://doi.org/10.1016/j.engappai.2009.04.002.
Pourfarhady Myankooh, Y., and S. Shafiei. 2016. “Specific strategy for determination of feasible domain of heat exchanger networks with no stream splitting and its assessment by application of ACOR algorithm.” Appl. Therm. Eng. 104 (Jul): 791–803. https://doi.org/10.1016/j.applthermaleng.2016.05.115.
Rahimikhoob, A. 2008. “Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment.” Irrig. Sci. 27 (1): 35–39. https://doi.org/10.1007/s00271-008-0119-y.
Rahimikhoob, A. 2009. “Estimating daily pan evaporation using artificial neural network in a semi-arid environment.” Theor. Appl. Climatol. 98 (1–2): 101–105. https://doi.org/10.1007/s00704-008-0096-3.
Rahimikhoob, A. 2010. “Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran.” Theor. Appl. Climatol. 101 (1–2): 83–91. https://doi.org/10.1007/s00704-009-0204-z.
Shiri, J., and O. Kisi. 2011. “Application of artificial intelligence to estimate daily pan evaporation using available and estimated climatic data in the Khozestan Province (South Western Iran).” J. Irrig. Drain. Eng. 137 (7): 412–425. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000315.
Shirsath, P. B., and A. K. Singh. 2010. “A comparative study of daily pan evaporation estimation using ANN, regression and climate based models.” Water Resour. Manage. 24 (8): 1571–1581. https://doi.org/10.1007/s11269-009-9514-2.
Singh, C. L., K. L. Baishnab, and C. H. Anandini. 2017. “Analysis and optimization of noises of an analog circuit via PSO algorithms.” Microsyst. Technol. 25 (5): 1793–1807. https://doi.org/10.1007/s00542-017-3573-8.
Socha, K., and M. Dorigo. 2008. “Ant colony optimization for continuous domains.” Eur. J. Oper. Res. 185 (3): 1155–1173. https://doi.org/10.1016/j.ejor.2006.06.046.
Sonmez, M. 2011. “Artificial bee colony algorithm for optimization of truss structures.” Appl. Soft Comput. 11 (2): 2406–2418. https://doi.org/10.1016/j.asoc.2010.09.003.
Sudheer, K. P., A. K. Gosain, D. Rangan, and S. M. Saheb. 2002. “Modeling evaporation using an artificial neural network algorithm.” Hydrol. Processes 16 (16): 3189–3202. https://doi.org/10.1002/hyp.1096.
Tabari, H., S. Marofi, A. Aeini, P. H. Talaee, and K. Mohammadi. 2011. “Trend analysis of reference evapotranspiration in the western half of Iran.” Agric. For. Meteorol. 151 (2): 128–136. https://doi.org/10.1016/j.agrformet.2010.09.009.
Tabari, H., S. Marofi, and A. A. Sabziparvar. 2010. “Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression.” Irrig. Sci. 28 (5): 399–406. https://doi.org/10.1007/s00271-009-0201-0.
Taylor, K. E. 2001. “Summarizing multiple aspects of model performance in a single diagram.” J. Geophys. Res. Atmos. 106 (D7): 7183–7192. https://doi.org/10.1029/2000JD900719.
Terzi, O. 2013. “Daily pan evaporation estimation using gene expression programming and adaptive neural-based fuzzy inference system.” Neural Comput. Appl. 23 (3–4): 1035–1044. https://doi.org/10.1007/s00521-012-1027-x.
Terzi, O., and M. E. Keskin. 2005. “Modeling of daily pan evaporation.” J. Appl. Sci. 5 (2): 368–372. https://doi.org/10.3923/jas.2005.368.372.
Tijani, I. B., R. Akmeliawati, A. Legowo, and A. Budiyono. 2014. “Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution.” Eng. Appl. Artif. Intell. 33 (Aug): 99–115. https://doi.org/10.1016/j.engappai.2014.04.003.
Trajkovic, S., B. Todorovic, and M. Stankovic. 2003. “Forecasting of reference evapotranspiration by artificial neural networks.” J. Irrig. Drain. Eng. 129 (6): 454–457. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:6(454).
Traore, S., and A. Guven. 2012. “Regional-specific numerical models of evapotranspiration using gene-expression programming interface in Sahel.” Water Resour. Manage. 26 (15): 4367–4380. https://doi.org/10.1007/s11269-012-0149-3.
Turkes, M., and U. M. Sumer. 2004. “Spatial and temporal patterns of trends and variability in diurnal temperature ranges of Turkey.” Theor. Appl. Climatol. 77 (3–4): 195–227.
Valiantzas, J. D. 2006. “Simplified versions for the Penman evaporation equation using routine weather data.” J. Hydrol. 331 (3–4): 690–702. https://doi.org/10.1016/j.jhydrol.2006.06.012.
Valiantzas, J. D. 2013. “Simplified reference evapotranspiration formula using an empirical impact factor for Penman’s aerodynamic term.” J. Hydrol. Eng. 18 (1): 108–114. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000590.
Wang, L., O. Kisi, M. Zounemat-Kermani, and H. Li. 2017a. “Pan evaporation modeling using six different heuristic computing methods in different climates of China.” J. Hydrol. 544 (Jan): 407–427. https://doi.org/10.1016/j.jhydrol.2016.11.059.
Wang, L., Z. Niu, O. Kisi, C. A. Li, and D. Yu. 2017b. “Pan evaporation modeling using four different heuristic approaches.” Comput. Electron. Agric. 140 (Aug): 203–213. https://doi.org/10.1016/j.compag.2017.05.036.
Wang, Z., P. Wu, X. Zhao, and X. Cao. 2014. “GANN models for reference evapotranspiration estimation developed with weather data from different climatic regions.” Theor. Appl. Climatol. 116 (3–4): 481–489. https://doi.org/10.1007/s00704-013-0967-0.
Whitley, D. 1994. “A genetic algorithm tutorial.” Stat. Comput. 4 (2): 65–85. https://doi.org/10.1007/BF00175354.
Yang, X.-S. 2008. Nature-inspired metaheuristic algorithms. Frome, UK: Luniver Press.
Zounemat-Kermani, M., O. Kisi, and T. Rajaee. 2013. “Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff.” Appl. Soft Comput. 13 (12): 4633–4644. https://doi.org/10.1016/j.asoc.2013.07.007.
Zounemat-Kermani, M., and A. Mahdavi-Meymand. 2019. “Hybrid meta-heuristics artificial intelligence models in simulating discharge passing the piano key weirs.” J. Hydrol. 569 (Feb): 12–21. https://doi.org/10.1016/j.jhydrol.2018.11.052.
Zounemat-Kermani, M., and M. Scholz. 2014. “Modeling of dissolved oxygen applying stepwise regression and a template-based fuzzy logic system.” J. Environ. Eng. 140 (1): 69–76. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000780.
Zounemat-Kermani, M., and M. Teshnehlab. 2008. “Using adaptive neuro-fuzzy inference system for hydrological time series prediction.” Appl. Soft Comput. 8 (2): 928–936. https://doi.org/10.1016/j.asoc.2007.07.011.
Information & Authors
Information
Published In
Copyright
©2019 American Society of Civil Engineers.
History
Received: Apr 1, 2018
Accepted: Apr 25, 2019
Published online: Jul 18, 2019
Published in print: Oct 1, 2019
Discussion open until: Dec 18, 2019
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.