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Flood forecasting using a hybrid extreme learning machine-particle swarm optimization algorithm (ELM-PSO) model

  • Sagnik AnupamEmail author
  • Padmini Pani
Original Article
  • 5 Downloads

Abstract

Flood forecasting in India is carried out by the determination of the water level at flood-forecasting stations. The level forecasts are issued once water levels in a station reach a predefined warning level, which helps local authorities to determine response measures to the floods. A new approach has been explored in this paper, which involves using the mean daily gauge heights, mean daily rainfall, and the mean daily river discharge values of prior days to forecast the mean gauge heights up to 4 days in advance. These features were used as input for an extreme learning machine (ELM) regression model. The number of units in the ELM was optimized to obtain the maximum coefficient of determination using the particle swarm optimization algorithm (PSO) to create a hybrid ELM-PSO model. Gauge, rainfall, and discharge data of 4 decades from the Jenapur flood-forecasting station (Brahmani river, Odisha) and the Anandpur station (Baitarani river, Odisha) were used to create models for mean gauge height prediction. These models were then cross-validated using tenfold cross-validation, with mean-squared error (MSE) and the coefficient of determination (R-squared) as parameters for evaluation of the models. The models show promising results, with the 1-day-in-advance model having MSE 0.14 and R-squared 0.85 for Jenapur and MSE 0.23 and R-squared 0.75 for Anandpur.

Keywords

Flood forecasting Machine learning Optimization Extreme learning machine Particle swarm optimization 

Notes

Acknowledgements

We are grateful to the Central Water Commission (CWC) and the Odisha State Disaster Management Authority (OSDMA) for their support in acquiring the river system data. We would also like to thank the Department of Science and Technology of the Government of India, the Indo-US Science and Technology Forum, and Intel India, the organizers of IRIS 2018, where this study was initially presented. We would also like to thank the Society for Science and the Public, organizers of ISEF 2019, held at Phoenix, Arizona, where this study was presented as well.

References

  1. Amarnath G, Alahacoon N, Gismalla Y, Mohammed Y, Sharma BR, Smakhtin V (2016) Increasing early warning lead time through improved transboundary flood forecasting in the Gash river basin, Horn of Africa. In: Flood forecasting. Elsevier, pp 183–200.  https://doi.org/10.1016/B978-0-12-801884-2.00008-6 CrossRefGoogle Scholar
  2. Bhattacharya B, Solomatine DP (2005) Neural networks and M5 model trees in modelling water level-discharge relationship. Neurocomputing 63:381–396.  https://doi.org/10.1016/j.neucom.2004.04.016 CrossRefGoogle Scholar
  3. Campolo M, Andreussi P, Soldati A (1999) River flood forecasting with a neural network model. Water Resour Res 35(4):1191–1197.  https://doi.org/10.1029/1998WR900086 CrossRefGoogle Scholar
  4. Central Water Commission (2015) Policy and Advisory Technical Assistance 8089 IND Phase II—operational research to support mainstreaming of integrated flood management under climate change. (Vol. 2., Basin Flood Management Plan Brahmani-Baitarani, Odisha). http://mowr.gov.in/sites/default/files/NWM_OR-FM-CC_2015_Vol-2_0.pdf
  5. Central Water Commission (2019) Standard operating procedure for flood forecasting, April 2019. http://cwc.gov.in/sites/default/files/admin/SOP_ff_apr_19.pdf
  6. Eberhart R, Kennedy J (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948.  https://doi.org/10.1109/ICNN.1995.488968 CrossRefGoogle Scholar
  7. Faris H, Aljarah I, Mirjalili S, Castillo PA, Guervós JJM (2016) EvoloPy: an open-source nature-inspired optimization framework in Python. In: IJCCI (ECTA), pp 171–177. https://www.scitepress.org/Papers/2016/60482/60482.pdf
  8. Ghose DK (2018) Measuring discharge using back-propagation neural network: a case study on Brahmani river basin. In: Intelligent engineering informatics. Springer, pp 591–598.  https://doi.org/10.1007/978-981-10-7566-7_59 Google Scholar
  9. Huang G-B (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput 6(3):376–390.  https://doi.org/10.1007/s12559-014-9255-2 CrossRefGoogle Scholar
  10. Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Netw 2:985–990.  https://doi.org/10.1109/IJCNN.2004.1380068 CrossRefGoogle Scholar
  11. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501.  https://doi.org/10.1016/j.neucom.2005.12.126 CrossRefGoogle Scholar
  12. Jha R, Sharma KD, Singh VP (2008) Critical appraisal of methods for the assessment of environmental flows and their application in two river systems of India. KSCE J Civ Eng 12(3):213–219.  https://doi.org/10.1007/s12205-008-0213-y CrossRefGoogle Scholar
  13. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3rd international conference on learning representations (ICLR). arXiv:1412.6980
  14. Liong S-Y, Sivapragasam C (2002) Flood stage forecasting with support vector machines. J Am Water Resour Assoc 38(1):173–186.  https://doi.org/10.1111/j.1752-1688.2002.tb01544.x CrossRefGoogle Scholar
  15. Liu T, Ding Y, Cai X, Zhu Y, Zhang X (2017) Extreme learning machine based on particle swarm optimization for estimation of reference evapotranspiration. In: 2017 36th Chinese control conference (CCC), pp 4567–4572. IEEE.  https://doi.org/10.23919/ChiCC.2017.8028076
  16. Noymanee J, Nikitin NO, Kalyuzhnaya AV (2017) Urban pluvial flood forecasting using open data with machine learning techniques in Pattani basin. Proced Comput Sci 119:288–297.  https://doi.org/10.1016/j.procs.2017.11.187 CrossRefGoogle Scholar
  17. Parsaie A, Haghiabi AH (2017) Mathematical expression of discharge capacity of compound open channels using mars technique. J Earth Syst Sci 126(2):20.  https://doi.org/10.1007/s12040-017-0807-1 CrossRefGoogle Scholar
  18. Parsaie A, Najafian S, Omid MH, Yonesi H (2017a) Stage discharge prediction in heterogeneous compound open channel roughness. ISH J Hydraul Eng 23(1):49–56.  https://doi.org/10.1080/09715010.2016.1235471 CrossRefGoogle Scholar
  19. Parsaie A, Yonesi H, Najafian S (2017b) Prediction of flow discharge in compound open channels using adaptive neuro fuzzy inference system method. Flow Meas Instrum 54:288–297.  https://doi.org/10.1016/j.flowmeasinst.2016.08.013 CrossRefGoogle Scholar
  20. Parsaie A, Yonesi HA, Najafian S (2015) Predictive modeling of discharge in compound open channel by support vector machine technique. Model Earth Syst Environ 1(1–2):1.  https://doi.org/10.1007/s40808-015-0002-9 CrossRefGoogle Scholar
  21. Sun Z-L, Choi T-M, Au K-F, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46(1):411–419.  https://doi.org/10.1016/j.dss.2008.07.009 CrossRefGoogle Scholar
  22. Wan C, Xu Z, Pinson P, Dong ZY, Wong KP (2013) Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Trans Power Syst 29(3):1033–1044.  https://doi.org/10.1109/TPWRS.2013.2287871 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DPS RK PuramNew DelhiIndia
  2. 2.Centre for the Study of Regional DevelopmentJawaharlal Nehru UniversityNew DelhiIndia

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