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Daily Rainfall Prediction Using Nonlinear Autoregressive Neural Network

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Micro-Electronics and Telecommunication Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 106))

Abstract

In this paper, a prediction model using Nonlinear Autoregressive Neural Networks with external variables (NARX) was proposed in order to forecast daily rainfall at Hoa Binh city, Vietnam. For this aim, eight-year time series of meteorological data were first collected, involving temperature, wind speed, relative humidity, solar radiation as input variables and daily rainfall as output variable. NARX-based daily rainfall prediction model was then constructed and validated using various criteria such as coefficient of correlation (R), root mean squared error (RMSE) and mean absolute error (MAE). Results show a good statistical correlation between measured and predicted rainfall values, i.e., R = 0.8846, RMSE = 5.3793 mm, and MAE = 3.0218 mm. Therefore, it is reasonably stated that the developed model is promising for the forecast of daily rainfall.

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References

  1. Nemry F, Demirel H (2012) Impacts of climate change on transport: a focus on road and rail transport infrastructures. European Commission, Joint Research Centre (JRC), Institute for Prospective Technological Studies (IPTS)

    Google Scholar 

  2. Willems P, Olsson J, Arnbjerg-Nielsen K, Beecham S, Pathirana A, Gregersen IB, Madsen H, Nguyen V-T-V (2012) Impacts of climate change on rainfall extremes and urban drainage systems. IWA Publishing

    Google Scholar 

  3. Qing-Chang L, Zhong-Ren P, Junyi Z (2013) Critical transportation infrastructure identification and prioritization under flooding risks. In: Transportation research board 92nd annual meeting

    Google Scholar 

  4. Toda K (2007) Urban Flooding and Measures. J Disaster Res 2:143–152. https://doi.org/10.20965/jdr.2007.p0143

    Article  Google Scholar 

  5. Suarez P, Anderson W, Mahal V, Lakshmanan TR (2005) Impacts of flooding and climate change on urban transportation: a system wide performance assessment of the Boston Metro Area. Trans Res Part D: Transp Environ 10:231–244

    Article  Google Scholar 

  6. Pregnolato M, Ford A, Wilkinson SM, Dawson RJ (2017) The impact of flooding on road transport: a depth-disruption function. Transp Res Part D: Transp Environ 55:67–81

    Article  Google Scholar 

  7. Keller S, Atzl A (2014) Mapping natural hazard impacts on road infrastructure—the extreme precipitation in Baden-Württemberg, Germany, June 2013. Int J Disaster Risk Sci 5:227–241

    Article  Google Scholar 

  8. Yadav AK, Chandel SS (2014) Solar radiation prediction using artificial neural network techniques: a review. Renew Sustain Energy Rev 33:772–781

    Article  Google Scholar 

  9. Sideratos G, Hatziargyriou ND (2007) An advanced statistical method for wind power forecasting. IEEE Trans Power Syst 22:258–265

    Article  Google Scholar 

  10. Wang W-C, Chau K-W, Cheng C-T, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374:294–306

    Article  Google Scholar 

  11. Chang F-J, Chiang Y-M, Tsai M-J, Shieh M-C, Hsu K-L, Sorooshian S (2014) Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information. J Hydrol 508:374–384

    Article  Google Scholar 

  12. Kisi O, Shiri J (2011) Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resour Manage 25:3135–3152

    Article  Google Scholar 

  13. Asklany SA, Elhelow K, Youssef IK, El-wahab MA (2011) Rainfall events prediction using rule-based fuzzy inference system. Atmos Res 101:228–236

    Article  Google Scholar 

  14. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick ØB (2013) Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province. Vietnam Nat Hazards 66:707–730. https://doi.org/10.1007/s11069-012-0510-0

    Article  Google Scholar 

  15. Billings SA (2013) Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. Wiley

    Google Scholar 

  16. Eccel E (2012) Estimating air humidity from temperature and precipitation measures for modelling applications. Meteorological Applications. 19:118–128. https://doi.org/10.1002/met.258

    Article  Google Scholar 

  17. Lepore C, Allen JT, Tippett MK (2016) Relationships between hourly rainfall intensity and atmospheric variables over the contiguous United States. J Climate 29:3181–3197. https://doi.org/10.1175/JCLI-D-15-0331.1

    Article  Google Scholar 

  18. Ruiz LGB, Cuéllar MP, Calvo-Flores MD, Jiménez MDCP (2016) An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings. Energies. 9:684. https://doi.org/10.3390/en9090684

    Article  Google Scholar 

  19. Boussaada Z, Curea O, Remaci A, Camblong H, Mrabet Bellaaj N (2018) A Nonlinear Autoregressive exogenous (NARX) neural network model for the prediction of the daily direct solar radiation. Energies 11:620. https://doi.org/10.3390/en11030620

    Article  Google Scholar 

  20. Pisoni E, Farina M, Carnevale C, Piroddi L (2009) Forecasting peak air pollution levels using NARX models. Eng Appl Artif Intell 22:593–602. https://doi.org/10.1016/j.engappai.2009.04.002

    Article  Google Scholar 

  21. Buitrago J, Asfour SS (2017) Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs. Energies 10:40. https://doi.org/10.3390/en10010040

    Article  Google Scholar 

  22. Marquardt D (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11:431–441. https://doi.org/10.1137/0111030

    Article  MathSciNet  MATH  Google Scholar 

  23. Le TT, Guilleminot J, Soize C (2016) Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite. Comput Methods Appl Mech Eng 303:430–449. https://doi.org/10.1016/j.cma.2015.10.006

  24. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res 30:79–82. https://doi.org/10.3354/cr030079

    Article  Google Scholar 

  25. Dao DV, Trinh SH, Ly H-B, Pham BT (2019) Prediction of compressive strength of geopolymer concrete using entirely steel slag aggregates: novel hybrid artificial intelligence approaches. Appl Sci 9:1113. https://doi.org/10.3390/app9061113

    Article  Google Scholar 

  26. Dao DV, Ly H-B, Trinh SH, Le T-T, Pham BT (2019) Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials 12:983. https://doi.org/10.3390/ma12060983

    Article  Google Scholar 

  27. Devi SR, Arulmozhivarman P, Venkatesh C, Agarwal P (2016) Performance comparison of artificial neural network models for daily rainfall prediction. Int J Autom Comput 13:417–427. https://doi.org/10.1007/s11633-016-0986-2

    Article  Google Scholar 

  28. Mahongo SB, Deo MC (2013) Using artificial neural networks to forecast monthly and seasonal sea surface temperature anomalies in the Western Indian Ocean. The Int J Ocean Climate Syst 4:133–150. https://doi.org/10.1260/1759-3131.4.2.133

    Article  Google Scholar 

  29. Ouyang H-T (2017) Nonlinear autoregressive neural networks with external inputs for forecasting of typhoon inundation level. Environ Monit Assess 189:376. https://doi.org/10.1007/s10661-017-6100-6

    Article  Google Scholar 

  30. Ang MRCO, Gonzalez RM, Castro PPM (2014) Multiple data fusion for rainfall estimation using a NARX-based recurrent neural network—the development of the REIINN model. IOP Conf Ser: Earth Environ Sci 17:012019. https://doi.org/10.1088/1755-1315/17/1/012019

  31. Abou Rjeily Y, Abbas O, Sadek M, Shahrour I, Hage Chehade F (2017) Flood forecasting within urban drainage systems using NARX neural network. Water Sci Technol 76:2401–2412. https://doi.org/10.2166/wst.2017.409

    Article  Google Scholar 

  32. Júnior J, Barreto GDA (2008) Multistep-ahead prediction of rainfall precipitation using the NARX network. Presented at the ESTSP’08, p 87

    Google Scholar 

  33. Noor HM, Ndzi D, Yang G, Safar NZM (2017) Rainfall-based river flow prediction using NARX in Malaysia. In: 2017 IEEE 13th international colloquium on signal processing its applications (CSPA), pp 67–72

    Google Scholar 

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Conflict of Interest

The author declares no conflict of interest.

Author information

Authors and Affiliations

  1. NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, 700000, Vietnam

    Vuong Minh Le, Binh Thai Pham & Tien-Thinh Le

  2. University of Transport Technology, Trieu Khuc, Thanh Xuan, Hanoi, 100000, Vietnam

    Hai-Bang Ly

  3. Faculty of Engineering, Vietnam National University of Agriculture, Gia Lam, Hanoi, 100000, Vietnam

    Lu Minh Le

Authors
  1. Vuong Minh Le
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  2. Binh Thai Pham
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  3. Tien-Thinh Le
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  4. Hai-Bang Ly
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  5. Lu Minh Le
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Corresponding author

Correspondence to Binh Thai Pham .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, India

    Devendra Kumar Sharma

  2. Department of Automatics and Applied Software, “Aurel Vlaicu” University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Multimedia and Virtual Reality, Institute of Information Technology, Vietnam National University, Hanoi, Vietnam

    Le Hoang Son

  4. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, India

    Rohit Sharma

  5. Department of Electrical and Electronics Engineering, Trakya University, Edirne, Turkey

    Korhan Cengiz

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Le, V.M., Pham, B.T., Le, TT., Ly, HB., Le, L.M. (2020). Daily Rainfall Prediction Using Nonlinear Autoregressive Neural Network. In: Sharma, D.K., Balas, V.E., Son, L.H., Sharma, R., Cengiz, K. (eds) Micro-Electronics and Telecommunication Engineering. Lecture Notes in Networks and Systems, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-15-2329-8_22

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  • DOI: https://doi.org/10.1007/978-981-15-2329-8_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2328-1

  • Online ISBN: 978-981-15-2329-8

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