Developing the artificial neural network–evolutionary algorithms hybrid models (ANN–EA) to predict the daily evaporation from dam reservoirs (original) (raw)
Adib A, Mahmoodi A (2017) Prediction of suspended sediment load using ANN GA conjunction model with Markov chain approach at flood conditions. KSCE J Civ Eng 21(1):447–457 Google Scholar
Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22(5):717–727 Google Scholar
Allawi MF, Aidan IA, El-Shafie A (2021) Enhancing the performance of data-driven models for monthly reservoir evaporation prediction. Environ Sci Pollut Res 28(7):8281–8295 Google Scholar
Ashrafzadeh A, Kisi O, Aghelpour P, Biazar SM, Masouleh MA (2020) Comparative study of time series models, support vector machines, and GMDH in forecasting long-term evapotranspiration rates in northern Iran. J Irrig Drain Eng 146(6):04020010 Google Scholar
Azar NA, Milan SG, Kayhomayoon Z (2021) The prediction of longitudinal dispersion coefficient in natural streams using LS-SVM and ANFIS optimized by Harris hawk optimization algorithm. J Contam Hydrol 240:103781 Google Scholar
Bruton JM, McClendon RW, Hoogenboom G (2000) Estimating daily pan evaporation with artificial neural networks. T Asae 43(2):491–496 Google Scholar
Brutsaert WH (1982) Evaporation into the Atmosphere. D. Reidel, Dordrecht, p 299 Google Scholar
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73 Google Scholar
Dang NM, Tran Anh D, Dang TD (2019) ANN optimized by PSO and Firefly algorithms for predicting scour depths around bridge piers. Eng Comput 35:1–11 Google Scholar
Dogan E, Isik S, Sandalci M (2007) Estimation of daily evaporation using artificial neural networks. Tek Dergi 18(2):4119–4131 Google Scholar
Dorigo M (1992) Optimization, learning and natural algorithms. PhD thesis. Politecnico di Milano, Italy
Floudas CA, Pardolos PM (2009) Encyclopedia of optimization, 2nd edn. Springer, Heidelberg Google Scholar
Eberhart R, Kennedy J (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948 Google Scholar
Ghorbani MA, Deo RC, Yaseen ZM, Kashani MH, Mohammadi B (2018) Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theoret Appl Climatol 133(3):1119–1131 Google Scholar
Goyal MK, Bharti B, Quilty J, Adamowski J, Pandey A (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 Google Scholar
Haghnegahdar L, Wang Y (2020) A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection. Neural Comput Appl 32(13):9427–9441 Google Scholar
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining inference and prediction, 2nd edn. California, Springer MATH Google Scholar
Haykin S (1999) Neural network and its application in IR, a comprehensive foundation, Upper Saddle Rever. Prentice Hall, New Jersey, p 842 (13, 775–781) Google Scholar
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872 Google Scholar
Holmes TR (2019) Remote sensing techniques for estimating evaporation. In extreme hydroclimatic events and multivariate hazards in a changing environment. Elsevier, Amsterdam, pp 129–143 Google Scholar
Jackson RD (1985) Evaluating evapotranspiration at local and regional scales. Proc IEEE 73(6):1086–1096 Google Scholar
Karimi-Googhari S (2010) Daily pan evaporation estimation using a neuro-fuzzy based model. Trends Agric Eng 2010:191–195 Google Scholar
Karkheiran S, Kabiri-Samani A, Zekri M, Azamathulla HM (2019) Scour at bridge piers in uniform and armored beds under steady and unsteady flow conditions using ANN-APSO and ANN-GA algorithms. ISH J Hydraul Eng 25:1–9 Google Scholar
Keskin ME, Terzi O (2006) Artificial neural network models of daily pan evaporation. J Hydrol Eng 11(1):65–70 Google Scholar
Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J Hydrol 351(3–4):299–317 Google Scholar
Kim S, Singh VP, Seo Y (2014) Evaluation of pan evaporation modeling with two different neural networks and weather station data. Theoret Appl Climatol 117(1–2):1–13 Google Scholar
Kiran NR, Ravi V (2008) Software reliability prediction by soft computing techniques. J Syst Softw 81(4):576–583 Google Scholar
Kirkpatrick S, Gellat CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:670–680 MathSciNet Google Scholar
Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140 Google Scholar
Kisi O (2005) Discussion of ‘“Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey.”’ Hydrol Sci J 50(4):727–728 Google Scholar
Kisi O (2006) Daily pan evaporation modelling using a neuro-fuzzy computing technique. J Hydrol 329(3–4):636–646 Google Scholar
Kisi O (2009) Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks. Hydrol Process 23(2):213–223 Google Scholar
Makridakis S, Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski R, Winkler R (1982) The accuracy of extrapolation (time series) methods: results of a forecasting competition. J Forecasting 1(2):111–153 Google Scholar
Malik A, Kumar A (2015) Pan evaporation simulation based on daily meteorological data using soft computing techniques and multiple linear regression. Water Resour Manage 29(6):1859–1872 Google Scholar
Milan SG, Roozbahani A, Azar NA, Javadi S (2021) Development of adaptive neuro fuzzy inference system–evolutionary algorithms hybrid models (ANFIS-EA) for prediction of optimal groundwater exploitation. J Hydrol 598:126258 Google Scholar
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67 Google Scholar
Moayedi H, Gör M, Lyu Z, Bui DT (2020) Herding Behaviors of grasshopper and Harris hawk for hybridizing the neural network in predicting the soil compression coefficient. Measurement 152:107389 Google Scholar
Moghaddamnia A, Gosheh MG, Nuraie M, Mansuri MA, Han D (2010) Performance evaluation of LLR, SVM, CGNN and BFGSNN models to evaporation estimation. Energy Environ Eng S 5:108–113 Google Scholar
Najafzadeh M, Tafarojnoruz A, Lim SY (2017) Prediction of local scour depth downstream of sluice gates using data-driven models. ISH J Hydraul Eng 23(2):195–202 Google Scholar
Nourani V, Elkiran G, Abdullahi J (2019) Multi-station artificial intelligence based ensemble modelling of reference evaporationspiration using pan evaporation measurements. J Hydrol 577:1–20 Google Scholar
Nourani V, Sayyah Fard M (2012) Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Adv Eng Softw 47(1):127–146 Google Scholar
Piri J et al (2009) Daily pan evaporation modeling in a hot and dry climate. J Hydrol Eng 14(8):803–811 Google Scholar
Samadianfard S, Hashemi S, Kargar K, Izadyar M, Mostafaeipour A, Mosavi A et al (2020) Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm. Energy Rep 6:1147–1159 Google Scholar
Sammen SS, Ghorbani MA, Malik A, Tikhamarine Y, AmirRahmani M, Al-Ansari N, Chau KW (2020) Enhanced artificial neural network with harris hawks optimization for predicting scour depth downstream of ski-jump spillway. Appl Sci 10(15):5160 Google Scholar
Samui P, Dixon B (2012) Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs. Hydrol Process 26(9):1361–1369 Google Scholar
Sanikhani H, Kisi O, Nikpour MR, Dinpashoh Y (2012) Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques. Water Resour Manage 26(15):4347–4365 Google Scholar
Seifi A, Riahi H (2018) Estimating daily reference evapotranspiration using hybrid gamma test-least square support vectormachine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran. J Water Clim Change. https://doi.org/10.2166/wcc.2018.003 Article Google Scholar
Sharifan H, Ghahreman B, Alizadeh A, Mirlatifi SM (2006) Comparion of the different methods of estimated reference evapotranspiration (compound and temperature) with standard method and analysis of aridity effects. J Agric Sci Nat Resour 13:19–30 (In Persian) Google Scholar
Sudheer KP, Gosain AK, Mohana Rangan D, Saheb SM (2002) Modelling evaporation using an artificial neural network algorithm. Hydrol Process 16(16):3189–3202 Google Scholar
Talbi EG (2009) Metaheuristics: from design to implementation. Wiley, Chichester MATH Google Scholar
Tikhamarine Y, Malik A, Kumar A, Souag-Gamane D, Kisi O (2019) Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrol Sci J 64(15):1824–1842 Google Scholar
Tran-Ngoc H, Khatir S, Ho-Khac H, De Roeck G, Bui-Tien T, Wahab MA (2021) Efficient Artificial neural networks based on a hybrid metaheuristic optimization algorithm for damage detection in laminated composite structures. Compos Struct 262:113339 Google Scholar
Zhou Y, Niu Y, Luo Q, Jiang M (2020) Teaching learning-based whale optimization algorithm for multi-layer perceptron neural network training [J]. Math Biosci Eng 17(5):5987–6025 Google Scholar