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Artificial Neural Network Approach to Flood Forecasting in the Vu Gia–Thu Bon Catchment, Vietnam

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Advances in Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1287))

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Abstract

Flooding in the Vu Gia-Thu Bon catchment has destroyed critical facilities, such as infrastructure and housing. This study develops an application of an Artificial Neural Network (ANN) to forecast the flow at the Nong Son gauging station in the catchment. The ANN model uses rainfall data at upstream locations to estimate flows at downstream point. Architectures of the ANN model are adjusted to calculate flooding. Daily rainfall at Tra My, Tien Phuoc, Hiep Duc and Nong Son between 1991 and 2010 is used to predict flooding at Nong Son. The analysis shows that the ANN is a reliable method to forecast the flood in the Vu Gia-Thu Bon catchment, where there is a lack of a wide range of accurate data, particularly hydrological, meteorological and geological data.

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References

  1. Navrud, S., Huu Tuan, T., Duc Tinh, B.: Estimating the welfare loss to households from natural disasters in developing countries: a contingent valuation study of flooding in Vietnam. Glob. Health Action 5(1), 17609 (2012)

    Article  Google Scholar 

  2. Sanz Ramos, M., Amengual, A., Bladé i Castellet, E., Romero, R., Roux, H.: Flood forecasting using a coupled hydrological and hydraulic model (based on FVM) and highresolution meteorological model. In: E3S Web of Conferences, Volume 40 (2018): River Flow 2018-Ninth International Conference on Fluvial Hydraulics, pp. 1–8 (2018)

    Google Scholar 

  3. Wheater, H., Sorooshian, S., Sharma, K.D.: Hydrological Modelling in Arid and Semi-Arid Areas. Cambridge University Press, Cambridge (2007)

    Book  Google Scholar 

  4. Devia, G.K., Ganasri, B., Dwarakish, G.: A review on hydrological models. Aquat. Procedia 4(1), 1001–1007 (2015)

    Article  Google Scholar 

  5. Vo, N.D., Gourbesville, P.: Application of deterministic distributed hydrological model for large catchment: a case study at Vu Gia Thu Bon catchment, Vietnam. J. Hydroinformatics 18(5), 885–904 (2016)

    Article  Google Scholar 

  6. Abbott, M.B., Bathurst, J.C., Cunge, J.A., O’Connell, P.E., Rasmussen, J.: An introduction to the European Hydrological System—Systeme Hydrologique Europeen, “SHE”, 1: history and philosophy of a physically-based, distributed modelling system. J. Hydrol. 87(1–2), 45–59 (1986)

    Article  Google Scholar 

  7. Abbott, M., Bathurst, J., Cunge, J., O’connell, P., Rasmussen, J.: An introduction to the European Hydrological System—Systeme Hydrologique Europeen,“SHE”, 2: structure of a physically-based, distributed modelling system. J. Hydrol. 87(1–2), 61–77 (1986)

    Article  Google Scholar 

  8. Thirumalaiah, K., Deo, M.: Real-time flood forecasting using neural networks. Comput.-Aided Civ. Infrastruct. Eng. 13(2), 101–111 (1998)

    Article  Google Scholar 

  9. Campolo, M., Andreussi, P., Soldati, A.: River flood forecasting with a neural network model. Water Resour. Res. 35(4), 1191–1197 (1999)

    Article  Google Scholar 

  10. Wurbs, R.A.: Computer models for water resources planning and management. Army Engineer Inst for Water Resources Fort Belvoir VA (1994)

    Google Scholar 

  11. Thirumalaiah, K., Deo, M.: River stage forecasting using artificial neural networks. J. Hydrol. Eng. 3(1), 26–32 (1998)

    Article  Google Scholar 

  12. Filipova, V.: Urban flooding in Gothenburg-A MIKE 21 study. TVVR12/5010 (2012)

    Google Scholar 

  13. Sharma, A.K., Thakkar, P., Adhyaru, D.M., Zaveri, T.H.: Gujarati handwritten numeral recognition through fusion of features and machine learning techniques. Int. J. Comput. Syst. Eng. 3(1–2), 35–47 (2017)

    Article  Google Scholar 

  14. Musarra, G., et al.: Detection, identification, and tracking of objects hidden from view with neural networks. In: Advanced Photon Counting Techniques XIII, vol. 10978, p. 1097803. International Society for Optics and Photonics (2019)

    Google Scholar 

  15. Taleb Bahmed, I., Harichane, K., Ghrici, M., Boukhatem, B., Rebouh, R., Gadouri, H.: Prediction of geotechnical properties of clayey soils stabilised with lime using artificial neural networks (ANNs). Int. J. Geotech. Eng. 13(2), 191–203 (2019)

    Article  Google Scholar 

  16. Falah, F., Rahmati, O., Rostami, M., Ahmadisharaf, E., Daliakopoulos, I.N., Pourghasemi, H.R.: Artificial neural networks for flood susceptibility mapping in data-scarce urban areas. In: Spatial Modeling in GIS and R for Earth and Environmental Sciences, pp. 323–336. Elsevier (2019)

    Google Scholar 

  17. Silva Santos, K.M., Celeste, A.B., El-Shafie, A.: ANNs and inflow forecast to aid stochastic optimization of reservoir operation. J. Appl. Water Eng. Res. 7(4), 314–323 (2019)

    Article  Google Scholar 

  18. Bayramoglu, M.F., Basarir, C.: International diversified portfolio optimization with artificial neural networks: an application with foreign companies listed on NYSE. In: Machine Learning Techniques for Improved Business Analytics, pp. 201–223. IGI Global (2019)

    Google Scholar 

  19. Dorofki, M., Elshafie, A.H., Jaafar, O., Karim, O.A., Mastura, S.: Comparison of artificial neural network transfer functions abilities to simulate extreme runoff data. Int. Proc. Chem. Biol. Environ. Eng. 33, 39–44 (2012)

    Google Scholar 

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

    Article  Google Scholar 

  21. Shamseldin, A.Y.: Application of a neural network technique to rainfall-runoff modelling. J. Hydrol. 199(3–4), 272–294 (1997)

    Article  Google Scholar 

  22. Abarghouei, H.B., Hosseini, S.Z.: Using exogenous variables to improve precipitation predictions of ANNs in arid and hyper-arid climates. Arab. J. Geosci. 9(15) (2016). Article number: 663. https://doi.org/10.1007/s12517-016-2679-0

  23. Rogers, L.L., Dowla, F.U.: Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour. Res. 30(2), 457–481 (1994)

    Article  Google Scholar 

  24. Rajaee, T., Mirbagheri, S.A., Zounemat-Kermani, M., Nourani, V.: Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Sci. Total Environ. 407(17), 4916–4927 (2009)

    Article  Google Scholar 

  25. Heidari, E., Sobati, M.A., Movahedirad, S.: Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemometr. Intell. Lab. Syst. 155, 73–85 (2016)

    Article  Google Scholar 

  26. Karunanithi, N., Grenney, W.J., Whitley, D., Bovee, K.: Neural networks for river flow prediction. J. Comput. Civ. Eng. 8(2), 201–220 (1994)

    Article  Google Scholar 

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Acknowledgement

This work was supported by The University of Danang, University of Science and Technology, code number of Project: T2020-02-47.

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Authors and Affiliations

  1. The University of Danang – University of Technology and Education, Da Nang, Vietnam

    Duy Vu Luu

  2. The University of Danang – University of Economics, Da Nang, Vietnam

    Thi Ngoc Canh Doan

  3. The University of Danang, University of Science and Technology, Da Nang, Vietnam

    Ngoc Duong Vo

Authors
  1. Duy Vu Luu
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  2. Thi Ngoc Canh Doan
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  3. Ngoc Duong Vo
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Corresponding author

Correspondence to Ngoc Duong Vo .

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Editors and Affiliations

  1. Wroclaw University of Economics and Business, Wrocław, Poland

    Marcin Hernes

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  3. University of Newcastle, Newcastle, Australia

    Edward Szczerbicki

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Luu, D.V., Doan, T.N.C., Vo, N.D. (2020). Artificial Neural Network Approach to Flood Forecasting in the Vu Gia–Thu Bon Catchment, Vietnam. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_50

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  • DOI: https://doi.org/10.1007/978-3-030-63119-2_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63118-5

  • Online ISBN: 978-3-030-63119-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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