Rainfall-river discharge modelling for flood forecasting using Artificial Neural Network (ANN)

• Autorzy: Obasi Arinze A. , Ogbu Kingsley N. , Orakwe Chukwuemeka L. , Ahaneku Isiguzo E.

Identyfikatory

DOI

10.24425/jwld.2019.127050

• Języki publikacji: EN

Abstrakty

EN

This study is aimed at evaluating the applicability of Artificial Neural Network (ANN) model technique for river discharge forecasting. Feed-forward multilayer perceptron neural network trained with back-propagation algorithm was employed for model development. Hydro-meteorological data for the Imo River watershed, that was collected from the Anambra-Imo River Basin Development Authority, Owerri – Imo State, South-East, Nigeria, was used to train, validate and test the model. Coefficients of determination results are 0.91, 0.91 and 0.93 for training, validation and testing periods respectively. River discharge forecasts were fitted against actual discharge data for one to five lead days. Model results gave R² values of 0.95, 0.95, 0.92, 0.96 and 0.94 for first, second, third, fourth, and fifth lead days of forecasts, respectively. It was generally observed that the R2 values decreased with increase in lead days for the model. Generally, this technique proved to be effective in river discharge modelling for flood forecasting for shorter lead-day times, especially in areas with limited data sets.

Słowa kluczowe

EN

artificial neural network (ANN); rainfall; flood forecasting; river discharge

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2020
• Tom: no. 44
• Strony: 98--105
• Opis fizyczny: Bibliogr. 23 poz., rys.

Twórcy

autor

Obasi Arinze A.

• Nnamdi Azikiwe University, Department of Agricultural and Bioresources Engineering, Ifite Road, 420110, Awka, Anambra State, Nigeria

autor

Ogbu Kingsley N.

• Centre for Development Research, University of Bonn, Germany

autor

Orakwe Chukwuemeka L.

• Nnamdi Azikiwe University, Awka, Anambra State, Nigeria

autor

Ahaneku Isiguzo E.

• Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria

Bibliografia

• ABRAHART R.J., ANCTIL F., COULIBALY P., DAWSON C.W., MOUNT N.J., SEE L.M., SHAMSELDIN A.Y., SOLOMATINE D.P., TOTH E., WILBY R.L. 2016. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Progress in Physical Geography, School of Geography, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
• ABRAHART R.J., KNEALE P.E., SEE L.M. 2005. Neural networks for hydrological modeling [online]. Leiden/London/New York/ Philadelphia/Singapore. A.A. Balkema Publishers. [Access 5.10.2018]. Available at: https://books.google.pl/books?id=_ZYIXrcqfWcC&pg=PP5&lpg=PP5&dq=http://balkema.tandf.co.uk+Neural+networks+for+hydrological+modeling&source=bl&ots=6bGdKYAMqs&sig=ACfU3U1YIGknrWyNmbzmAhVqhosKHUkWAIkQ6AEwAXoECAsQAQ#v=onepage&q=http%3A%2F%2Fbalkema.tandf.co.uk%20Neural%20networks%20for%20hydrological%20modeling&f=false
• Alyuda NeuroIntelligence 2.2(577) 2018. Alyuda Research Inc. Available for one-pc free trial download at https://www.alyuda.com/products/neurointelligence/download.htm
• AMANGABARA G.T., OBENADE M. 2015. Flood vulnerability assessment of Niger Delta States relative to 2012 flood disaster in Nigeria. American Journal Of Environmental Protection. Vol. 3. No. 3 p. 76–83. DOI 10.12691/envi-3-3-3.
• AWU J.I., MBAJIORGU C.C., OGUNLELA A.O., KASALI M.Y., ADEMILUYI Y.S., JAMES D.D. 2017. Optimization of neural network architecture and transfer functions for rainfallriverflow modelling. Journal of Environmental Hydrology. Vol. 25. Iss. 8 p. 1–15.
• BABY N., VARIJA K. 2016. Modeling of rainfall–runoff relationship using Artificial Neural Networks. International Journal on Recent and Innovation Trends in Computing and Communication. Vol. 4. Iss. 12 p. 233–237.
• DAWSON C.W., WILBY R.L. 2001. Hydrological modeling using Artificial Neural Networks. Progress in Physical Geography. Vol. 25. Iss. 1 p. 80–108.
• DERDOUR A., BOUANANI A., BABAHAMED K. 2018. Modeling rainfall runoff relations using HEC-HMS in a semi-arid region: Case study in Ain Sefra Watershed, Ksour Mountains (SW Algeria). Journal of Water and Land Development. No. 36 p. 45–55. DOI 10.2478/jwld-2018-0005.
• HSU K., GUPTA H.V., SOROOSHAIN S. 1995. Artificial Neural Network modeling of the rainfall-runoff process. Water Resources Research. Vol. 31. Iss. 10 p. 2517–2530. DOI 10.14257/ijhit.2016.9.3.24.
• HUSSAIN D., USMANI A., VERMA D.K., JAMAL F., KHAN M.A. 2017. Rainfall runoff modeling using Artificial Neural Network. International Journal of Advance Research, Ideas and Innovations in Technology. Vol. 3. Iss. 6 p. 1528–1533.
• JAFAR R., SHAHROUR I., JURAN I. 2010. Application of Artificial Neural Networks (ANN) to model the failure of urban water mains. Mathematical and Computer Modeling. Vol. 51. Iss. 9–10 p. 1170–1180. DOI 10.1016/j.mcm.2009.12.033.
• JOSHI J., PATEL V.M. 2011. Rainfall-runoff modeling using Artificial Neural Network (a literature review). National Conference on Recent Trends in Engineering and Technology. B.V.M. Engineering College, V.V. Nagar, Gujarat, India. 13–14 May 2011 p. 1–4.
• KISI O., OZKAN C., AKAY B. 2012. Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm. Journal of Hydrology. Vol. 428–429 p. 94–103. DOI 10.1016/j.jhydrol.2012.01.026.
• LEGATES D.R., MCCABE G.J. 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydro-climatic model validation. Water Resources Research. Vol. 35. Iss. 1 p. 233–241. DOI 10.1029/1998WR900018.
• MACHADO F., MINE M., KAVISKI E., FILL H. 2011. Monthly rainfall–runoff modeling using Artificial Neural Networks. Hydrological Sciences Journal. Vol. 56. Iss. 3 p. 349–361.
• MOTAMEDNIA M., NOHEGAR A., MALEKIAN A., ASADI H., TAVASOLI A., SAFARI M., KARIMI-ZARCHI K. 2015. Daily river flow forecasting in a semi-arid region using two data – driven models. Desert 20-1 p. 11–21.
• OBASI A.A., OGBU K.N., NDULUE E.L., OGWO V.N., MBAJIORGU C.C. 2017. Prediction of the impacts of climate changes on the stream flow of Ajali River watershed using SWAT model. Nigerian Journal of Technology (NIJOTECH). Vol. 36. No. 4 p. 1286–1295. DOI 10.4314/njt.v36i4.39.
• RAGHUWANSHI N.S., SINGH R., REDDY L.S. 2006. Runoff and sediment yield modeling using artificial neural networks: Upper Siwane River, India. Journal of Hydrologic Engineering. Vol. 11. Iss. 1. DOI 10.1061/(ASCE)1084-0699(2006)11:1(71).
• SARKAR R., KUMAR R. 2012. Artificial neural networks for eventbased rainfall-runoff modeling. Journal of Water Resource and Protection. Vol. 4. Iss. 10 p. 891–897. DOI 10.4236/jwarp.2012.410105.
• SOLAIMANI K. 2009. Rainfall-runoff prediction based on artificial neural network (A case study: Jarahi Watershed). American-Eurasian Journal of Agricultural and Environmental. Sciences. Vol. 5. Iss. 6 p. 856–865.
• RAJURKAR M.P., KOTHYARI U.C., CHAUBE U.C. 2004. Modeling of the daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology. Vol. 285. Iss. 1 p. 96–113. DOI 10.1016/j.jhydrol.2003.08.011.
• TOKAR S., MARKUS M. 2000. Precipitation-runoff modeling using artificial neural networks and conceptual models. Journal of Hydrologic Engineering. Vol. 5. Iss. 2 p. 156–161. DOI 10.1061/(ASCE)1084-0699(2000)5:2(156).
• VAROONCHOTIKUL P. 2003. Flood forecasting using Artificial Neural Network [online]. UNESCO-IHE Institute of Education. A.A. Balkema Publishers. ISBN 90 5809 631 9. [Access 5.10.2018]. Available at: https://pdfs.semanticscholar.org/3c32/c2479f197f2bf02d07f923ce7760a7e15041.pdf

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).