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Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus

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Abstract

The target of the current paper was to examine the performance of three Markovian and seasonal based artificial neural network (ANN) models for one-step ahead and three-step ahead prediction of monthly precipitation which is the most important parameter of any hydrological study. The models proposed here are feed forward neural network (FFNN, as a classic ANN-based models), Wavelet-ANN (WANN, as a hybrid model), and Emotional-ANN (EANN, as a modern generation of ANN-based models). The models were used to precipitation prediction of seven stations located in the Northern Cyprus. Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station’s precipitation through its own data at previous time steps, while in scenario 2, the central station’s data were also imposed into the models in addition to each station’s data, as exogenous inputs. The obtained results showed the better performance of the EANN model in comparison with other models (FFNN and WANN) especially in three-step ahead prediction. The superiorities of the EANN model over other models are due to its ability in dealing with error magnification in multi-step ahead prediction. Also, the results indicated that the performance of the scenario 2 was better than scenario 1, showing improvement of modeling efficiency up to 17% and 26% in calibration and verification steps, respectively.

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References

  • Abbot J, Marohasy J (2012) Application of artificial neural networks to rainfall forecasting in Queensland, Australia. Adv Atmos Sci 29(4):717–730

    Article  Google Scholar 

  • Anmala J, Zhang B, Govindaraju RS (2000) Comparison of ANNs and empirical approaches for predicting watershed runoff. J Water Resour Plann Manage 126(3):156–166

  • Adamowski J, Chan E, Prasher S, Ozga-Zielinski B, Sliusareva A (2012) Comparison of multiple linear and non-linear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour Res 48(1):1156–1168

    Article  Google Scholar 

  • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural networks in hydrology. II: hydrologic applications. J Hydrol Eng 5(2):124–137

    Article  Google Scholar 

  • Bisht D, Joshi MC, Mehta A (2015) Prediction of monthly rainfall of Nainital region using artificial neural network and support vector machine. Int J Adv Res Innov Ideas Educ 1(3):2395–4396

    Google Scholar 

  • Chau KW (2017) Use of meta-heuristic techniques in rainfall-runoff modelling. Water 9(3):186

    Article  Google Scholar 

  • Danandeh Mehr A (2018) Month ahead rainfall forecasting using gene expression programming. J Earth Environ Sci 1(2):63–70

    Google Scholar 

  • Danandeh Mehr A, Kahya E, Olyaie E (2013) Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. J Hydrol 505:240–249

    Article  Google Scholar 

  • Danandeh Mehr A, Nourani V, Hrnjica B, Molajou A (2017) A binary genetic programming model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events. J Hydrol 555:397–406

    Article  Google Scholar 

  • Danandeh Mehr A, Jabarnejad M, Nourani V (2019a) Pareto-optimal MPSA-MGGP: a new gene-annealing model for monthly rainfall forecasting. J Hydrol 571:406–415

    Article  Google Scholar 

  • Danandeh Mehr A, Nourani V, Khosrowshahi VK, Ghorbani MA (2019b) A hybrid support vector regression-firefly model for monthly rainfall forecasting. Int J Environ Sci Technol 16(1):335–346

    Article  Google Scholar 

  • Devi SR, Arulmozhivarman P, Venkatesh C (2017) ANN based rainfall prediction - a tool for develo** a landslide early warning system. In: Advancing culture of living with landslides- workshop on world landslide forum. pp 175–182

  • Ghorbani MA, Kazempour R, Chau KW, Shamshirband S, Ghazvinei PT (2018) Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, northern Iran. Eng Appl Comput Fluid Mech 12(1):724–737

    Google Scholar 

  • Guhathakurta, P (2008) Long lead monsoon rainfall prediction for meteorological sub-divisions of India using deterministic artificial neural network model. Meteorology and Atmospheric Physics 101(2):93–108

  • Griggs C, Pearson C, Manning SW, Lorentzen B (2014) A 250-year annual precipitation reconstruction and drought assessment for Cyprus from Pinus brutia ten. Tree-rings. Int J Climatol 34:2702–2714

    Article  Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2:359–366. https://doi.org/10.1007/s00704-019-02904-x

  • Khalili N, Khodashenas SR, Davary K, Mousavi B, Karimaldini F (2016) Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study. Arab J Geosci 9(624)

  • Kisi O, Cimen M (2012) Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng Appl Artif Intell 25(4):783–792

    Article  Google Scholar 

  • Legates DR, McCabe GJ Jr (1999) Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241

    Article  Google Scholar 

  • Lotfi E, Akbarzadeh-T MR (2014) Practical emotional neural networks. Neural Netw 59:61–72

    Article  Google Scholar 

  • Lotfi E, Akbarzadeh-T MR (2016) A winner-take-all approach to emotional neural networks with universal approximation property. Inf Sci 347:369–388

    Article  Google Scholar 

  • Mehdizadeh S, Behmanesh J, Khalili K (2018) New approaches for estimation of monthly rainfall based on GEP-ARCH and ANN-ARCH hybrid models. Water Resour Manag 32(2):527–545

    Article  Google Scholar 

  • Nourani V, Molajou A (2017) Application of a hybrid association rules/decision tree model for drought monitoring. Glob Planet Chang 159:37–45

    Article  Google Scholar 

  • Nourani V, Sattari MT, Molajou A (2017) Threshold-based hybrid data mining method for long-term maximum precipitation forecasting. Water Resour Manag 31(9):2645–2658

    Article  Google Scholar 

  • Nourani V, Davanlou Tajbakhsh A, Molajou A (2018a) Data mining based on wavelet and decision tree for rainfall-runoff simulation. Hydrol Res 50(1):75–84

    Article  Google Scholar 

  • Nourani V, Razzaghzadeh Z, Hosseini Baghanam A, Molajou A (2018b) ANN-based statistical downscaling of climatic parameters using decision tree predictor screening method. Theor Appl Climatol. https://doi.org/10.1007/s00704-018-2686-z

  • Nourani V, Molajou A, Davanlou Tajbakhsh A, Najafi H (2019a) A wavelet based data mining technique for suspended sediment load modeling. Water Resour Manag 33(5):1769–1784

    Article  Google Scholar 

  • Nourani V, Davanlou Tajbakhsh A, Molajou A, Gokcekus H (2019b) Hybrid Wavelet-M5 Model tree for rainfall-runoff modeling. J Hydrol Eng 24(5). https://doi.org/10.1061/(ASCE)HE.1943-5584.0001777

  • Nourani V, Molajou A, Najafi H, Mehr AD (2019c) Emotional ANN (EANN): a new generation of neural networks for hydrological modeling in IoT. In: Artificial Intelligence in IoT. Springer, Cham, pp 45–61

  • Partal T, Cigizoglu HK (2008) Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. J Hydrol 358(3–4):317–331

    Article  Google Scholar 

  • Price C, Michaelides S, Pashiardis S, Alperta P (1999) Long term changes in diurnal temperature range in Cyprus. Atmos Res 51(2):85–98

    Article  Google Scholar 

  • Sharghi E, Nourani V, Najafi H, Molajou A (2018) Emotional ANN (EANN) and wavelet-ANN (WANN) approaches for Markovian and seasonal based modeling of rainfall-runoff process. Water Resour Manag 32(10):3441–3456

    Article  Google Scholar 

  • Sharghi E, Nourani V, Molajou A, Najafi H (2019) Conjunction of emotional ann (eann) and wavelet transform for rainfall-runoff modeling. J Hydroinf 21:136–152. https://doi.org/10.2166/hydro.2018.054

    Article  Google Scholar 

  • Shiri J, Kisi O (2010) Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. J Hydrol 394:486–493

    Article  Google Scholar 

  • Wu CL, Chau KW (2011) Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis. J Hydrol 399(3–4):394–409

    Article  Google Scholar 

  • Yang HH, Vuuren SV, Sharma S, Hermansky H (2000) Relevance of timefrequency features for phonetic and speaker-channel classification. Speech Comm 31:35–50

    Article  Google Scholar 

  • Yaseen ZM, Sulaiman SO, Deo RC, Chau KW (2019a) 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. J Hydrol 569:387–408

    Article  Google Scholar 

  • Yaseen ZM, Ebtehaj I, Kim S, Sanikhani H, Asadi H, Ghareb MI, Bonakdari H, Mohtar WH, Al-Ansari N, Shahid S (2019b) Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. Water 11(3):502

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, 29 Bahman Ave., Tabriz, 5166616471, Iran

    Vahid Nourani

  2. Faculty of Civil and Environmental Engineering, Near East University, P.O. Box: 99138, Lefkosa, TRNC, via Mersin 10, Turkey

    Vahid Nourani

  3. Department of Water Resources Engineering, Faculty of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Amir Molajou

  4. Department of Computer Engineering, Faculty of Engineering, Near East University, PO BOX: 99138, Lefkosa, TRNC, Turkey

    Selin Uzelaltinbulat

  5. Department of Electrical and Electronic Eng., Faculty of Engineering, Near East University, PO BOX: 99138, Lefkosa, TRNC, Turkey

    Fahreddin Sadikoglu

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  1. Vahid Nourani
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  2. Amir Molajou
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  3. Selin Uzelaltinbulat
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  4. Fahreddin Sadikoglu
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Correspondence to Vahid Nourani.

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Nourani, V., Molajou, A., Uzelaltinbulat, S. et al. Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus. Theor Appl Climatol 138, 1419–1434 (2019). https://doi.org/10.1007/s00704-019-02904-x

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