Abstract
The accuracy level for reservoir evaporation prediction is an important issue for decision making in the water resources field. The traditional methods for evaporation prediction could encounter numerous obstacles owing to the effect of several parameters on the shape of the evaporation pattern. The current research presented modern model called the Coactive Neuro-Fuzzy Inference System (CANFIS). Modification for such model has been achieved for enhancing the evaporation prediction accuracy. Genetic algorithm was utilized to select the effective input combination. The efficiency of the proposed model has been compared with popular artificial intelligence models according to several statistical indicators. Two different case studies Aswan High Dam (AHD) and Timah Tasoh Dam (TTD) have been considered to explore the performance of the proposed models. It is concluded that the modified GA-CANFIS model is better than GA-ANFIS, GA-SVR, and GA-RBFNN for evaporation prediction for both case studies. GA-CANFIS attained minimum RMSE (15.22 mm month−1 for AHD, 8.78 mm month−1 for TTD), minimum MAE (12.48 mm month−1 for AHD, 5.11 mm month−1 for TTD), and maximum determination coefficient (0.98 for AHD, 0.95 for TTD).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abraham A, Khan MR (2004) Neuro-fuzzy paradigms for intelligent energy management. Springer, Berlin, Heidelberg, pp 285–314
Alecsandru C, Ishak S (2004) Hybrid model-based and memory-based traffic prediction system. Transp Res Rec J Transp Res Board 1879:59–70. https://doi.org/10.3141/1879-08
Aljanabi QA, Chik Z, Allawi MF, et al (2017) Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment. Neural Comput Appl 1–11 . doi: https://doi.org/10.1007/s00521-016-2807-5
Allawi MF, Jaafar O, Ehteram M, Mohamad Hamzah F, el-Shafie A (2018a) Synchronizing artificial intelligence models for operating the dam and reservoir system. Water Resour Manag 32:1–17. https://doi.org/10.1007/s11269-018-1996-3
Allawi MF, Jaafar O, Mohamad Hamzah F, Abdullah SMS, el-shafie A (2018b) Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models. Environ Sci Pollut Res 25:13446–13469. https://doi.org/10.1007/s11356-018-1867-8
Allawi MF, Jaafar O, Mohamad Hamzah F, et al (2017) Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region. Theor Appl Climatol 1–19 . https://doi.org/10.1007/s00704-017-2292-5
Allawi MF, Jaafar O, Mohamad Hamzah F, Koting SB, Mohd NSB, el-Shafie A (2019) Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance. Knowledge-Based Syst 163:907–926. https://doi.org/10.1016/J.KNOSYS.2018.10.013
Aytek A (2009) Co-active neurofuzzy inference system for evapotranspiration modeling. Soft Comput 13:691–700. https://doi.org/10.1007/s00500-008-0342-8
Broomhead D, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks
Chauhan S, Shrivastava RK (2009) Performance evaluation of reference evapotranspiration estimation using climate based methods and artificial neural networks. Water Resour Manag 23:825–837. https://doi.org/10.1007/s11269-008-9301-5
Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37:63–68. https://doi.org/10.1016/j.advengsoft.2005.05.002
Clarke J, McLay L, McLeskey JT (2014) Comparison of genetic algorithm to particle swarm for constrained simulation-based optimization of a geothermal power plant. Adv Eng Inform 28:81–90. https://doi.org/10.1016/j.aei.2013.12.003
Deswal S, Mathematical MP (2008) Artificial neural network based modeling of evaporation losses in reservoirs. Int J Civ Environ Eng 2:18–22
El-Shafie A, Noureldin AE, Taha MR, Basri H (2008) Neural network model for Nile River inflow forecasting based on correlation analysis of historical inflow data. J Appl Sci 8:4487–4499. https://doi.org/10.3923/jas.2008.4487.4499
Hanafy TOS, Hanafy TOS (2014) A new algorithm to model highly nonlinear system based coactive neuro fuzzy inference system 94:9–20
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366. https://doi.org/10.1016/0893-6080(89)90020-8
Jain SK, Nayak PC, Sudheer KP (2008) Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrol Process 22:2225–2234. https://doi.org/10.1002/hyp.6819
Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing jang: a computational approach to learning and machine intelligence 640
Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236. https://doi.org/10.1016/0925-2312(95)00039-9
Kerh T, Lee CS (2006) Neural networks forecasting of flood discharge at an unmeasured station using river upstream information. Adv Eng Softw 37:533–543. https://doi.org/10.1016/j.advengsoft.2005.11.002
Kim S, Kim H (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J Hydrol 351:299–317
Kişi Ö (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
Lohani AK, Kumar R, Singh RD (2012) Hydrological time series modeling: a comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J Hydrol 442:23–35. https://doi.org/10.1016/j.jhydrol.2012.03.031
Memarian H, Pourreza Bilondi M, Rezaei M (2016) Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran). Theor Appl Climatol 125:541–554. https://doi.org/10.1007/s00704-015-1532-9
Moghaddamnia A, Gousheh M, Piri J, Amin S (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water
Nourani V (2009) Using artificial neural networks (ANNs) for sediment load forecasting of Talkherood river mouth. J Urban Environ Eng 3:1–6. https://doi.org/10.4090/juee.2009.v3n1.001006
Omar MH, El-Bakry MM (1981) Estimation of evaporation from the lake of the Aswan High Dam (Lake Nasser) based on measurements over the lake. Agric Meteorol 23:293–308. https://doi.org/10.1016/0002-1571(81)90115-1
Patil S, Valunjkar S (2016) Utility of coactive neuro-fuzzy inference system for runoff prediction in comparison with multilayer perception. Int J Eng Res 8:156–160. https://doi.org/10.17950/ijer/v5i1/036
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
Rajaee T, Mirbagheri SA, Zounemat-Kermani M, Nourani V (2009) Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Sci Total Environ 407:4916–4927. https://doi.org/10.1016/j.scitotenv.2009.05.016
Saemi M, Ahmadi M (2008) Integration of genetic algorithm and a coactive neuro-fuzzy inference system for permeability prediction from well logs data. Transp Porous Media 71:273–288. https://doi.org/10.1007/s11242-007-9125-4
Salih SQ, Allawi MF, Yousif AA, Armanuos AM, Saggi MK, Ali M, Shahid S, al-Ansari N, Yaseen ZM, Chau KW (2019) Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of Nasser Lake in Egypt. Eng Appl Comput Fluid Mech 13:878–891. https://doi.org/10.1080/19942060.2019.1647879
Shirsath PB, Singh AK (2010) A comparative study of daily pan evaporation estimation using ANN, regression and climate based models. Water Resour Manag 24:1571–1581. https://doi.org/10.1007/s11269-009-9514-2
Tabari H, Hosseinzadeh Talaee P, Abghari H (2012) Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron. Meteorog Atmos Phys 116:147–154. https://doi.org/10.1007/s00703-012-0184-x
Tan SBK, Shuy EB, Chua LHC (2007) Modelling hourly and daily open-water evaporation rates in areas with an equatorial climate. Hydrol Process 21:486–499. https://doi.org/10.1002/hyp.6251
Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239:132–147. https://doi.org/10.1016/S0022-1694(00)00344-9
Acknowledgments
The author greatly acknowledge the datasets provided by the Nile Water Authority (NWA) and Aswan High Dam Authority (AHDA), Ministry of Water Resources and Irrigation, Egypt, and Water Resources Management and Hydrology Division, Department of Irrigation and Drainage (DID), Malaysia.
Author information
Authors and Affiliations
Contributions
Conceptualization: Mohammed Falah Allawi and Ahmed El-Shafie. Software and writing of the original draft version: Mohammed Falah Allawi. Review and editing: Ibraheem Abdallah Aidan and Ahmed El-Shafie.
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no competing interests.
Ethical approval
We acknowledge that the current research has been conducted ethically and the final shape of the research has been agreed by all authors.
Consent to participate
The authors consent to participate in this research study.
Consent to publish
The authors consent to publish the current research in ESPR journal.
Additional information
Responsible editor: Marcus Schulz
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Allawi, M.F., Aidan, I.A. & El-Shafie, A. Enhancing the performance of data-driven models for monthly reservoir evaporation prediction. Environ Sci Pollut Res 28, 8281–8295 (2021). https://doi.org/10.1007/s11356-020-11062-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-020-11062-x