Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

Abbot J, Marohasy J (2012) Application of artificial neural networks to rainfall forecasting in Queensland, Australia. Adv Atmos Sci 29:717–730. https://doi.org/10.1007/s00376-012-1259-9

Abhishek K, Kumar A, Ranjan R, Kumar S (2012) A rainfall prediction model using artificial neural network. 2012 I.E. Control Syst Grad Res Colloq, pp 82–87. https://doi.org/10.1109/ICSGRC.2012.6287140

Ahmed NK, Atiya AF, Gayar N El, El-Shishiny H (2010) An empirical comparison of machine learning models for time series forecasting. Econ Rev 29:594–621. https://doi.org/10.1080/07474938.2010.481556

Altunkaynak A, Nigussie TA (2015) Prediction of daily rainfall by a hybrid wavelet-season-neuro technique. J Hydrol 529:287–301. https://doi.org/10.1016/j.jhydrol.2015.07.046

Awadallah MA, Bayoumi EHE, Soliman HM (2009) Adaptive deadbeat controllers for brushless DC drives using PSO and ANFIS techniques. J Electr Eng 60:3–11

Azimi H, Bonakdari H, Ebtehaj I, Michelson DG (2016) A combined adaptive neuro-fuzzy inference system–firefly algorithm model for predicting the roller length of a hydraulic jump on a rough channel bed. Neural Comput & Applic. https://doi.org/10.1007/s00521-016-2560-9

Çekmiş A, Hacihasanoǧlu I, Ostwald MJ (2014) A computational model for accommodating spatial uncertainty: predicting inhabitation patterns in open-planned spaces. Build Environ 73:115–126. https://doi.org/10.1016/j.buildenv.2013.11.023

Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? -arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250. https://doi.org/10.5194/gmd-7-1247-2014

Chang FJ, Chang YT (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29:1–10. https://doi.org/10.1016/j.advwatres.2005.04.015

Clements MP, Franses PH, Swanson NR (2004) Forecasting economic and financial time-series with non-linear models. Int J Forecast 20:169–183. https://doi.org/10.1016/j.ijforecast.2003.10.004

Dastorani MT, Moghadamnia A, Piri J, Rico-Ramirez M (2010) Application of ANN and ANFIS models for reconstructing missing flow data. Environ Monit Assess 166:421–434. https://doi.org/10.1007/s10661-009-1012-8

De Gooijer JG, Hyndman RJ (2006) 25 years of time series forecasting. Int J Forecast 22:443–473. https://doi.org/10.1016/j.ijforecast.2006.01.001

Deo RC, Şahin M (2016) An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland. Environ Monit Assess 188(2):90

Dumedah G (2014) Toward essential union between evolutionary strategy and data assimilation for model diagnostics: an application for reducing the search space of optimization problems using hydrologic genome map. Environ Model Softw 69:342–352. https://doi.org/10.1016/j.envsoft.2014.09.025

Ebtehaj I, Bonakdari H (2014) Performance evaluation of adaptive neural fuzzy inference system for sediment transport in sewers. Water Resour Manag 28(13):4765–4779

El-Shafie A, Taha MR, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resour Manag 21:533–556. https://doi.org/10.1007/s11269-006-9027-1

El-Shafie A, Jaafer O, Seyed A (2011) Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia. Int J Phys Sci 6:2875–2888

Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50:987–1007. https://doi.org/10.2307/1912773

Fahimi F, Yaseen ZM, El-shafie A (2016) Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theor Appl Climatol:1–29. https://doi.org/10.1007/s00704-016-1735-8

Gasim MB, Toriman ME, Idris M et al (2013) River flow conditions and dynamic state analysis of Pahang river. Am J Appl Sci 10:42–57. https://doi.org/10.3844/ajassp.2013.42.57

Ghorbani MA, Shamshirband S, Zare Haghi D et al (2017) Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil Tillage Res 172:32–38. https://doi.org/10.1016/j.still.2017.04.009

Goyal MK, Bharti B, Quilty J et al (2014) Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, fuzzy logic, and ANFIS. Expert Syst Appl 41:5267–5276. https://doi.org/10.1016/j.eswa.2014.02.047

Harun Z, Jantan A, Jasin B et al (2014) Pahang River map of river. J Asian Earth Sci 44:177–194. https://doi.org/10.5327/Z23174889201400040002

Hipni A, El-shafie A, Najah A et al (2013) Daily forecasting of dam water levels: comparing a support vector machine (SVM) model with adaptive Neuro fuzzy inference system (ANFIS). Water Resour Manag 27:3803–3823. https://doi.org/10.1007/s11269-013-0382-4

Hong Y, Hsu K, Sorooshian S, Gao X (2005) Self-organizing nonlinear output (SONO): a neural network suitable for cloud patch-based rainfall estimation at small scales. Water Resour Res. https://doi.org/10.1029/2004WR003142

Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541

Kamarian S, Yas MH, Pourasghar A, Daghagh M (2014) Application of firefly algorithm and ANFIS for optimisation of functionally graded beams. J Exp Theor Artif Intell 26:197–209. https://doi.org/10.1080/0952813X.2013.813978

Kaur A, Kaur A (2012) Comparison of Mamdani-type and Sugeno-type fuzzy inference Systems for air Conditioning System. Int J Soft Comput Eng 2:323–325

Ketabchi H, Ataie-Ashtiani B (2015) Evolutionary algorithms for the optimal management of coastal groundwater: a comparative study toward future challenges. J Hydrol 520:193–213. https://doi.org/10.1016/j.jhydrol.2014.11.043

Krishna Kumar K, Hoerling M, Rajagopalan B (2005) Advancing dynamical prediction of Indian monsoon rainfall. Geophys Res Lett 32:1–4. https://doi.org/10.1029/2004GL021979

Legates DR, Mccabe GJ (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241

McMillan H, Jackson B, Clark M et al (2011) Rainfall uncertainty in hydrological modelling: an evaluation of multiplicative error models. J Hydrol 400:83–94. https://doi.org/10.1016/j.jhydrol.2011.01.026

Mellit A, Kalogirou SA (2011) ANFIS-based modelling for photovoltaic power supply system: a case study. Renew Energy 36:250–258. https://doi.org/10.1016/j.renene.2010.06.028

Michalski RS, Carbonell JG, Mitchell TM (2013) Machine learning: an artificial intelligence approach. Springer Science \& Business Media, New York

Najah A, El-Shafie A, Karim O, Jaafar O (2012) Water quality prediction model utilizing integrated wavelet-ANFIS model with cross-validation. Neural Comput & Applic 21:833–841. https://doi.org/10.1007/s00521-010-0486-1

Nayak D, Mahapatra A, Mishra P (2013) A survey on rainfall prediction using artificial neural network. Int J Comput 72:32–40. https://doi.org/10.5120/12580-9217

Nhu HN, Nitsuwat S, Sodanil M (2013) Prediction of stock price using an adaptive Neuro-fuzzy inference system trained by firefly algorithm. 2013 Int Comput Sci Eng Conf ICSEC 2013, pp 302–307. https://doi.org/10.1109/ICSEC.2013.6694798

Palit AK, Popovic D (2005) Computational intelligence in time series forecasting: theory and engineering applications (advances in industrial control). Springer-Verlag New York, Inc., Secaucus

Poskitt DS, Tremayne AR (1986) The selection and use of linear and bilinear time series models. Int J Forecast 2:101–114. https://doi.org/10.1016/0169-2070(86)90033-6

Prasad R, Deo RC, Li Y, Maraseni T (2017) Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm. Atmos Res 197:42–63

Shamshirband S, Mohammadi K, Tong CW et al (2016) A hybrid SVM-FFA method for prediction of monthly mean global solar radiation. Theor Appl Climatol 125:53–65. https://doi.org/10.1007/s00704-015-1482-2

Sojitra MA, Purohit RC, Pandya PA (2015) Comparative study of daily rainfall forecasting models using adaptive-neuro fuzzy inference system (ANFIS). Curr World Environ 10(2):529

Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern SMC-15:116–132. https://doi.org/10.1109/TSMC.1985.6313399

Tong H, Lim KS (1980) Threshold autoregression, limit cycles and cyclical data. J R Stat Soc Ser B 42:245–292

Venkata Ramana R, Krishna B, Kumar SR, Pandey NG (2013) Monthly rainfall prediction using wavelet neural network analysis. Water Resour Manag 27:3697–3711. https://doi.org/10.1007/s11269-013-0374-4

Werbos PJ (1988) Generalization of backpropagation with application to a recurrent gas market model. Neural Netw 1:339–356. https://doi.org/10.1016/0893-6080(88)90007-X

Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194

Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Biomed Comput 2(2):78–84. https://doi.org/10.1504/IJBIC.2010.032124

Yaseen ZM, El-shafie A, Jaafar O et al (2015) Artificial intelligence based models for stream-flow forecasting: 2000–2015. J Hydrol 530:829–844. https://doi.org/10.1016/j.jhydrol.2015.10.038

Yaseen ZM, Jaafar O, Deo RC, Kisi O, Adamowski J, Quilty J, El-Shafie A (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. J Hydrol 542:603–614

Zimmer A, Schmidt A, Ostfeld A, Minsker B (2015) Evolutionary algorithm enhancement for model predictive control and real-time decision support. Environ Model Softw 69:330–341. https://doi.org/10.1016/j.envsoft.2015.03.005