Skip to main content
SpringerLink
Log in

Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

One of the most challenging tasks in rainfall prediction is designing a reliable computational methodology owing the random and stochastic characteristics of time-series. In this study, the potential of five different data-driven models including Multilayer Perceptron (MLP), Least Square Support Vector Machine (LSSVM), Neuro-fuzzy, Hammerstein-Weiner (HW) and Autoregressive Integrated Moving Average (ARIMA) were employed for multi-station (Hien, Thank My, Hoi Khanh, Ai Nghia and Cai Lau) prediction of daily rainfall in the Vu Gia-Thu Bon River basin in Central Vietnam. Subsequently, hybrid ARIMA-MLP, ARIMA-LSSVM, ARIMA-NF and ARIMA-HW models were also utilized to predict the daily rainfall at these stations. The results were evaluated in terms of widely used performance criteria, viz.: determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC). Besides, the Taylor diagram is also used to examine and compare the similarity between the observed and predicted rainfall. The quantitative analysis indicated that the HW model increased the prediction accuracy by 5%, 3% and 2% at Hien, Ai Nghia and Cau Lau stations, respectively, compared to the other models. Likewise, the NF model increased the prediction accuracy at Thanh My and Hoi Khanh stations in contrast to the other models in terms of the mean absolute error. Also, the results of hybrid ARIMA-NF and ARIMA-HW models showed the best performance in terms of predictive skills and verified to increase the prediction accuracy in comparison to the single models.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abba SI, Hadi SJ, Abdullahi J (2017) River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia Computer Science 120:75–82

    Article  Google Scholar 

  • Adnan RM, Malik A, Kumar A, Parmar KS, Kisi O (2019) Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arab J Geosci 12:606–614. https://doi.org/10.1007/s12517-019-4781-6

    Article  Google Scholar 

  • Akrami SA, Nourani V, Hakim SJS (2014) Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang gates dam. Water Resour Manag 28(10):2999–3018

    Article  Google Scholar 

  • Alizamir M, Kisi O, Zounemat-Kermani M (2018) Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrol Sci J 63(1):63–73

    Article  Google Scholar 

  • Al-Zahrani MA, Abo-Monasar A (2015) Urban residential water demand prediction based on artificial neural networks and time series models. Water Resour Manag 29(10):3651–3662

    Article  Google Scholar 

  • Bartoletti N, Casagli F, Marsili-Libelli S, Nardi A, Palandri L (2018) Data-driven rainfall/runoff modelling based on a neuro-fuzzy inference system. Environ Model Softw 106:35–47

    Article  Google Scholar 

  • Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, Hoboken

    Google Scholar 

  • Brown K (2008) Hydrological and hydraulic modelling for the Ord River irrigation study

  • Bustami R, Bessaih N, Bong C, Suhaili S (2007) Artificial neural network for precipitation and water level predictions of Bedup River. IAENG Int J Comput Sci 34(2):228–233

    Google Scholar 

  • Che Z, Purushotham S, Cho K, Sontag D, Liu Y (2018) Recurrent neural networks for multivariate time series with missing values. Sci Rep 8(1):1–12

    Article  Google Scholar 

  • Choubin B, Khalighi-Sigaroodi S, Malekian A, Kişi Ö (2016) Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrol Sci J 61(6):1001–1009

    Article  Google Scholar 

  • Cramer S, Kampouridis M, Freitas AA, Alexandridis AK (2017) An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Syst Appl 85:169–181

    Article  Google Scholar 

  • Danandeh Mehr A, Nourani V, Karimi Khosrowshahi V, Ghorbani MA (2019) A hybrid support vector regression–firefly model for monthly rainfall forecasting. Int J Environ Sci Technol 16(1):335–346

    Article  Google Scholar 

  • Elkiran G, Nourani V, Abba SI, Abdullahi J (2018) Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river. Global J Environ Sci Manage 4(4):439–450

    Google Scholar 

  • Elkiran G, Nourani V, Abba SI (2019) Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. J Hydrol 577:123962

    Article  Google Scholar 

  • Gaya MS, Zango MU, Yusuf LA, Mustapha M, Muhammad B, Sani A, Tijjani A, Wahab NA, Khairi MTM (2017) Estimation of turbidity in water treatment plant using hammerstein-wiener and neural network technique. Indonesian Journal of Electrical Engineering and Computer Science 5(3):666–672

    Article  Google Scholar 

  • Ghorbani MA, Khatibi R, Goel A, FazeliFard MH, Azani A (2016) Modeling river discharge time series using support vector machine and artificial neural networks. Environ Earth Sci 75(8):1–13

    Article  Google Scholar 

  • Ghorbani MA, Deo RC, Yaseen ZM, Kashani H, Mohammadi B (2018) Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theor Appl Climatol 133(3–4):1119–1131

    Article  Google Scholar 

  • Ghose DK, Panda SS, Swain PC (2013) Prediction and optimization of runoff via ANFIS and GA. Alexandria Engineering Journal 52(2):209–220

    Article  Google Scholar 

  • Guo F (2004) A new identification method for Wiener and Hammerstein systems. For schungszentrum Karlsruhe. Retrieved from 10.23919/ECC.2003.7085284

  • Hadi SJ, Tombul M (2018) Forecasting daily streamflow for basins with different physical characteristics through data-driven methods. Water Resour Manag 32(10):3405–3422

    Article  Google Scholar 

  • Horton, P., Jaboyedoff, M., & Obled, C. (2018). Using genetic algorithms to optimize the analogue method for precipitation prediction in the Swiss Alps. J Hydrol, 556:1220–1231. https://doi.org/10.1016/j.jhydrol.2017.04.017

    Article  Google Scholar 

  • Hung NQ, Babel MS, Weesakul S, Tripathi NK (2009) An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol Earth Syst Sci 13(8):1413–1425

    Article  Google Scholar 

  • Jahani B, Mohammadi B (2019) A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran. Theor Appl Climatol 137(1–2):1257–1269

    Article  Google Scholar 

  • Kalteh AM (2019) Modular wavelet–extreme learning machine: a new approach for forecasting daily rainfall. Water Resour Manag 33:3831–3849. https://doi.org/10.1007/s11269-019-02333-5

    Article  Google Scholar 

  • Kim S, Singh VP (2014) Modeling daily soil temperature using data-driven models and spatial distribution. Theor Appl Climatol 118(3):465–479

    Article  Google Scholar 

  • Kumar P, Kendra KV, Gramvidhyapith L (2015) Daily rainfall forecasting using adaptive neuro- fuzzy inference system ( Anfis ) MODELS. Int J Sci Nature 6(3):382–388

    Google Scholar 

  • Lin GF, Jhong BC (2015) A real-time forecasting model for the spatial distribution of typhoon rainfall. J Hydrol 521:302–313

    Article  Google Scholar 

  • Malik A, Kumar A, Kisi O, Shiri J (2019a) Evaluating the performance of four different heuristic approaches with gamma test for daily suspended sediment concentration modeling. Environ Sci Pollut Res 26:22670–22687. https://doi.org/10.1007/s11356-019-05553-9

    Article  Google Scholar 

  • Malik A, Kumar A, Singh RP (2019b) Application of heuristic approaches for prediction of hydrological drought using multi-scalar Streamflow drought index. Water Resour Manag 33:3985–4006. https://doi.org/10.1007/s11269-019-02350-4

    Article  Google Scholar 

  • Meshram SG, Ghorbani MA, Deo RC, Kashani MH, Meshram C, Karimi V (2019) New approach for sediment yield forecasting with a two-phase feedforward neuron network-particle swarm optimization model integrated with the gravitational search algorithm. Water Resour Manag 33:2335–2356. https://doi.org/10.1007/s11269-019-02265-0

    Article  Google Scholar 

  • Nourani, V., Kisi, Ö., & Komasi, M. (2011). Two hybrid Artificial Intelligence approaches for modeling rainfall – runoff process. J Hydrol 402(1–2): 41–59. https://doi.org/10.1016/j.jhydrol.2011.03.002

    Article  Google Scholar 

  • Nourani V, Elkiran G, Abba SI (2018) Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach. Water Sci Technol 78(10):2064–2076

    Article  Google Scholar 

  • Nourani V, Elkiran G, Abdullahi J (2019) Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements. J Hydrol. https://doi.org/10.1016/j.jhydrol.2019.123958

    Article  Google Scholar 

  • Olyaie E, Zare Abyaneh H, Danandeh Mehr A (2017) A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River. Geosci Front 8(3):517–527

    Article  Google Scholar 

  • Qasem SN, Samadianfard S, Kheshtgar S et al (2019) Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics. https://doi.org/10.1080/19942060.2018.1564702

    Article  Google Scholar 

  • Sharifi SS, Delirhasannia R, Nourani V, Sadraddini AA, Ghorbani A (2009) Using artificial neural networks ( ANNs ) and adaptive Neuro-fuzzy inference system ( ANFIS ) for modeling and sensitivity analysis of effective rainfall, (2008):133–139

  • Solgi A, Zarei H, Nourani V, Bahmani R (2017) A new approach to flow simulation using hybrid models. Appl Water Sci 7(online):16. https://doi.org/10.1007/s13201-016-0515-z

    Article  Google Scholar 

  • Tayfur G, Singh VP (2006) ANN and fuzzy logic models for simulating event-based rainfall-runoff. J Hydraul Eng 132(12):1321–1330

    Article  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106(D7):7183–7192

    Article  Google Scholar 

  • Tikhamarine Y, Souag-Gamane D, Kisi O (2019) A new intelligent method for monthly streamflow prediction: hybrid wavelet support vector regression based on grey wolf optimizer (WSVR–GWO). Arab J Geosci 12:1–20. https://doi.org/10.1007/s12517-019-4697-1

    Article  Google Scholar 

  • Tsioptsias N, Tako A, Robinson S (2016) Model validation and testing in simulation: a literature review. OpenAccess Series in Informatics 50(6):6.1–6.11

    Google Scholar 

  • Yadav B, Ch S, Mathur S, Adamowski J (2017) Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction. Journal of Water and Land Development 32(1):103–112

    Article  Google Scholar 

  • Yaseen ZM, Ghareb MI, Ebtehaj I, Bonakdari H, Siddique R, Heddam S, Yusif AA, Deo R (2018) Rainfall pattern forecasting using novel hybrid intelligent model based ANFIS-FFA. Water Resour Manag 32(1):105–122

    Article  Google Scholar 

  • Yaseen ZM, Ebtehaj I, Kim S, Sanikhani H, Asadi H, Ghareb MI, Bonakdari H, Mohtar WHMW, Nadhir AA, Shahid S (2019) Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. Water (Switzerland) 11(3):502

    Google Scholar 

  • Zhang PG (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175

    Article  Google Scholar 

  • Zhang, X., Zwiers, F. W., Li, G., Wan, H., & Cannon, A. J. (2017). Complexity in estimating past and future extreme short-duration rainfall. Nature Geosci 10(4), 255–259.

  • Zhou T, Wang F, Yang Z (2017) Comparative analysis of ANN and SVM models combined with wavelet preprocess for groundwater depth prediction. Water (Switzerland) 9(10):781

    Google Scholar 

Download references

Acknowledgements

This study is funded by National project titled “Research on scientific basis and solution of artificial intelligence application to identify, support forecasting and warning some dangerous hydrometeorological phenomena in the context of climate change in Vietnam. Grant number: BDKH.34/16-20” and Ministry of Natural Resources and Environment of the project titled “Research and application of ECMWF products to establish the flood forecasting scenarios in main river basins in the Mid-Central region” grant number: TNMT.2018.05.35.

Author information

Authors and Affiliations

  1. Department of Hydraulic and Ocean Engineering, National Cheng-Kung University, Tainan, 701, Taiwan

    Quoc Bao Pham & Nguyen Thi Thuy Linh

  2. Department of Physical Planning Development, Yusuf Maitama Sule University Kano, Kano, Nigeria

    S. I. Abba

  3. Faculty of Pharmacy, Department of Analytical Chemistry, Near East University, 99138, Nicosia, Turkish Republic of Northern Cyprus

    Abdullahi Garba Usman

  4. Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam

    Nguyen Thi Thuy Linh

  5. Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India

    Vivek Gupta

  6. Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India

    Anurag Malik

  7. Research Institute of the University of Bucharest, 36-46 Bd. M. Kogalniceanu, 5th District, 050107, Bucharest, Romania

    Romulus Costache

  8. National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st District, 013686, 10 Bucharest, Romania

    Romulus Costache

  9. University of Science and Technology, The University of Danang, Danang, Vietnam

    Ngoc Duong Vo

  10. Sustainable Management of Natural Resources and Environment Research Group, Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Doan Quang Tri

Authors
  1. Quoc Bao Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. I. Abba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdullahi Garba Usman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nguyen Thi Thuy Linh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vivek Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anurag Malik
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Romulus Costache
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ngoc Duong Vo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Doan Quang Tri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Doan Quang Tri.

Ethics declarations

Conflict of Interest

The authors declare no conflicts of interest.

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

Pham, Q.B., Abba, S.I., Usman, A.G. et al. Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall. Water Resour Manage 33, 5067–5087 (2019). https://doi.org/10.1007/s11269-019-02408-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-019-02408-3

Keywords

Search

Navigation