Skip to main content

Advertisement

SpringerLink
Log in

Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling

  • Published:
Water Resources Management Aims and scope Submit manuscript

A Correction to this article was published on 15 June 2021

This article has been updated

Abstract

Rainfall-runoff (r-r) modeling at different time scales is considered as a significant issue in hydro-environmental planning. As a first hydrological implementation, for one-time-ahead r-r modeling of two watersheds with totally distinct climatic conditions, Genetic Algorithm (GA, as a global search technique) and Emotional Artificial Neural Network (EANN, as a new production of Artificial Intelligence (AI) based methods that simulated based on the brain neurophysiological structure) was combined. Determining the optimal architecture of AI-based networks is vital for increasing the accuracy of prediction by the network and also to reduce run-time. In the current study, GA has been implemented to choose the important features candidate as EANN input and automatically diagnose the optimal number of hidden nodes and hormones simultaneously. The acquired results indicated a better representation of the proposed hybrid GA-EANN model compared to the sole ANN and EANN. Numerical identification of obtained results revealed that the proposed hybrid GA-EANN model might enhance the better results than the EANN model up to 19% and 35% in terms of testing suitability criteria for Aji Chai and Murrumbidgee catchments, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Change history

References

  • Abrahart RJ, Anctil F, Coulibaly P, Dawson CW, Mount NJ, See LM, Shamseldin AY, Solomatine DP, Toth E, Wilby RL (2012) Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Prog Phys Geograph. 36(4):480–513

    Article  Google Scholar 

  • 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 Res Res. 48(1). https://doi.org/10.1029/2010WR009945

  • Adnan RM, Petroselli A, Heddam S, Santos CAG, Kisi O (2021) Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach. Natural Hazards 105(3):2987–3011

    Article  Google Scholar 

  • Ahmad F, Mat-Isa NA, Hussain Z, Boudville R, Osman MK (2010) Genetic algorithm - artificial neural network (GA-ANN) hybrid intelligence for cancer diagnosis. Second International Conference on Computational Intelligence, Communication Systems and Networks

  • 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 

  • Dawson C, Wilby R (2001) Hydrological modelling using artificial neural networks. Prog Phys Geograph. 25(1):80–108

    Article  Google Scholar 

  • Jain A, Srinivasulu S (2006) Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques. J Hydrol. 317(3):291–306

    Article  Google Scholar 

  • Kermani BG, White MW, Nagle HT (1995) Feature extraction by genetic algorithms for neural networks in breast cancer classification, Engineering in Medicine and Biology Society, IEEE 17th Annual Conference

  • Khashman A (2008) A modified backpropagation learning algorithm with added emtional coeeficent. IEEE Trans Neural Netw Learn Syst. 19:1896–1909

    Article  Google Scholar 

  • Kumar S, Roshni T, Himayoun D (2019) A comparison of emotional neural network (ENN) and artificial neural network (ANN) approach for rainfall-runoff modelling. Civil Eng J 5(10):2120–2130

    Article  Google Scholar 

  • Lewin DI (2001) Why is that computer laughing? IEEE Intell Systes. 16(5):79–81

    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 

  • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Modell Software. 15(1):101–124

    Article  Google Scholar 

  • Molajou A, Afshar A, Khosravi M, Soleimanian E, Vahabzadeh M, Variani HA (2021) A new paradigm of water, food, and energy nexus. Environ Sci Pollut Res:1–11. https://doi.org/10.1007/s11356-021-13034-1

  • Nayak PC, Sudheer K, Rangan D, Ramasastri K (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1):52–66

    Article  Google Scholar 

  • Nazif S, Karamouz M, Fallahi M, Rahimi M (2009) Long Lead Rainfall Prediction Using Statistical Downscaling and Articial Neural Network Modeling. Scientia Iranica 16(2):165–172

    Google Scholar 

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

    Article  Google Scholar 

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

  • Nourani V, Molajou A, Uzelaltinbulat S, Sadikoglu F (2019b) Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: Northern Cyprus. Theoretic Appl Climatol 138(3):1419–1434

    Article  Google Scholar 

  • Rezaie-Balf M, Zahmatkesh Z, Kim S (2017) Soft computing techniques for rainfall-runoff simulation: local non–parametric paradigm vs. model classification methods. Water Res Manag 31(12):3843–3865

    Article  Google Scholar 

  • Rohani M, Afshar MH, Moeini R (2015) Layout and Size Optimization of Sewer Networks by hybridizing the GHCA Model with Heuristic Algorithms. Scientia Iranica 22(6):2069–2082

    Google Scholar 

  • Safari MJS, Arashloo SR, Mehr AD (2020) Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm. J Hydrol 587:125014

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sharghi E, Nourani V, Molajou A, Najafi H (2018b) Conjunction of Emotional ANN (EANN) and wavelet transform for rainfall-runoff modeling. J hydroinform 21(1):136–152

    Article  Google Scholar 

  • Sharghi, E., Paknezhad, N. J., & Najafi, H. 2021. Assessing the effect of emotional unit of emotional ANN (EANN) in estimation of the prediction intervals of suspended sediment load modeling. Earth Science Informatics, 1–13

  • Temeng VA, Ziggah YY, Arthur CK (2020) A novel artificial intelligent model for predicting air overpressure using brain inspired emotional neural network. Int J Mining Sci Technol 30(5):683–689

    Article  Google Scholar 

  • Xiao, L., Wang Z. & Peng, X., 2009. Research on congestion control model and algorithm for high-speed network based on genetic neural network and intelligent PID, in Wireless Communications, Networking and Mobile Computing, 5th International Conference

  • Yaseen ZM, Naganna SR, Sa’adi Z, Samui P, Ghorbani MA, Salih SQ, Shahid S (2020) Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms. Water Res Manag 34(3):1075–1091

    Article  Google Scholar 

Download references

Availability of Data and Material

Not applicable.

Code availability.

Not applicable.

Author information

Authors and Affiliations

  1. Department of Water Resources Eng., Faculty of Civil Eng, Iran University of Science & Technology, Tehran, Iran

    Amir Molajou, Abbas Afshar & Mina Khosravi

  2. Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    Vahid Nourani

  3. Faculty of Civil and Environmental Engineering, Near East University, via Mersin 10, 99138, Nicosia, N Cyprus, Turkey

    Vahid Nourani

  4. Institute of Technology and Life Sciences, Raszyn, Poland

    Adam Brysiewicz

Authors
  1. Amir Molajou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vahid Nourani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abbas Afshar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mina Khosravi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adam Brysiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Amir Molajou: Conceptualization, Code Developer, Formal analysis, Writing - original draft.

Vahid Nourani: Supervision, Methodology, Review & Editing.

Abbas Afshar: Supervision, Methodology, Review & Editing.

Mina Khosravi: Formal analysis, Writing - original draft, Visualization.

Adam Brysiewicz: Visualization; Original draft; Review & Editing.

The authors give their full consent for the publication of this manuscript.

Corresponding author

Correspondence to Amir Molajou.

Ethics declarations

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Conflicts of Interest/Competing Interests

None.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Molajou, A., Nourani, V., Afshar, A. et al. Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling. Water Resour Manage 35, 2369–2384 (2021). https://doi.org/10.1007/s11269-021-02818-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-021-02818-2

Keywords

Search

Navigation