Skip to main content

Advertisement

SpringerLink
Log in

Rainfall prediction using optimally pruned extreme learning machines

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

Rainfall impacts local water quantity and quality. Accurate and timely prediction of rainfall is highly desirable in water management and hydrogeologic hazards mitigation, which is critical for the environmentally sustainable development. Previous rainfall prediction processes are complex and computationally costly for its intrinsic high uncertainty and variability. In this paper, a data-driven approach is applied to predict the rainfall based on historic data via time-series modeling and optimally pruned extreme learning machine (OP-ELM). The rainfall datasets collected from six counties within Three Georges Reservoir are utilized as case studies in this research, first, the rainfall data is pre-processed with outlier removal and missing value imputation, and the monthly ahead difference is computed based on the instant average of monthly rainfall. Next, the autocorrelation function and partial autocorrelation function were computed and the Ljung–Box test statistic is utilized to explore the significance of the historic lagged-series. All the statistically significant historic lags are selected as inputs for prediction algorithms. Last, an OP-ELM algorithm is developed to predict the monthly rainfall with tenfold cross validation. Four activation functions: the sigmoid, sine, hardlim, and radial basis function, are considered in the OP-ELM. The prediction performance is evaluated with metrics including mean absolute error, mean absolute percentage error, root mean square error, and max error rate. Overall, the computational results indicate the proposed framework outperforms the other benchmarking machine learning algorithms through six case studies in Three Gorges Reservoir, China.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Adhikary S, Muttil N, Yilmaz A (2016) Genetic-programming based ordinary kriging for spatial interpolation of rainfall. J Hydrol Eng ASCE 14:04015062

    Article  Google Scholar 

  • Camps-Valls G, Martín-Guerrero JD, Rojo-Alvarez JL, Soria-Olivas E (2004) Fuzzy sigmoid kernel for support vector classifiers. Neurocomputing 62:501–506

    Article  Google Scholar 

  • Cawley GC, Talbot NL (2003) Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognit 36(11):2585–2592

    Article  Google Scholar 

  • Chattopadhyay S, Chattopadhyay G (2008) Identification of the best hidden layer size for three-layered neural net in predicting monsoon rainfall in India. J Hydroinform 10(2):181–188

    Article  Google Scholar 

  • Chattopadhyay G, Midya SK, Chattopadhyay S (2020) Information theoretic study of the ground-level ozone and its precursors over Kolkata, India, during the summer monsoon. Iran J Sci Technol Trans Sci. https://doi.org/10.1007/s40995-020-01007-x

    Article  Google Scholar 

  • Deo RC, Şahin M (2015) Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia. Atmos Res 153:512–525

    Article  Google Scholar 

  • Esteves JT, de Souza Rolim G, Ferraudo AS (2019) Rainfall prediction methodology with binary multilayer perceptron neural networks. Clim Dyn 52(3–4):2319–2331

    Article  Google Scholar 

  • Grigorievskiy A, Miche Y, Ventelä AM, Séverin E, Lendasse A (2014) Long-term time series prediction using OP-ELM. Neural Netw 51:50–56

    Article  Google Scholar 

  • He Y, Kusiak A (2018) Performance assessment of wind turbines: data-derived quantitative metrics. IEEE Trans Sustain Energy 9(1):65–73

    Article  Google Scholar 

  • He Y, Kusiak A, Ouyang T, Teng W (2017) Data-driven modeling of truck engine exhaust valve failures: a case study. J Mech Sci Technol 31(6):2747–2757

    Article  Google Scholar 

  • He Y, Fei F, Wang W, Song X, Sun Z, Baek S (2018) Predicting manufactured shapes of a projection micro-stereolithography process via convolutional encoder-decoder networks. In: Proceedings of ASME 2018 international design engineering technical conferences & computers and information in engineering conference, Quebec City, Quebec, Canada

  • Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Their Appl 13(4):18–28

    Article  Google Scholar 

  • Horikawa SI, Furuhashi T, Uchikawa Y (1992) On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Trans Neural Netw 3(5):801–806

    Article  Google Scholar 

  • Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892

    Article  Google Scholar 

  • Kasun LLC, Zhou H, Huang GB, Vong CM (2013) Representational learning with extreme learning machine for big data. IEEE Intell Syst 28(6):31–34

    Google Scholar 

  • Kişi Ö (2009) Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting. Hydrol Process Int J 23(25):3583–3597

    Article  Google Scholar 

  • Labat D, Ababou R, Mangin A (2000) Rainfall–runoff relations for karstic springs. Part II: continuous wavelet and discrete orthogonal multiresolution analyses. J Hydrol 238(3–4):149–178

    Article  Google Scholar 

  • Lee T (2016) Wild bootstrap Ljung–Box test for cross correlations of multivariate time series. Econ Lett 147:59–62

    Article  Google Scholar 

  • Li H, Xu Q, He Y, Deng J (2018) Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models. Landslides 15(10):2047–2059

    Article  Google Scholar 

  • Li H, Xu Q, He Y et al (2020) Modeling and predicting reservoir landslide displacement with deep belief network and EWMA control charts: a case study in Three Gorges Reservoir. Landslides 17(3):693–707

    Article  Google Scholar 

  • Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411–1423

    Article  Google Scholar 

  • Liu H, Tian HQ, Li YF (2012) Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Appl Energy 98:415–424

    Article  Google Scholar 

  • Matsuda A, Kurosaki T (2019) Demand for temperature and rainfall index insurance in India. Agric Econ 50(3):353–366

    Article  Google Scholar 

  • McLeod A, Li W (1983) Diagnostic checking ARMA time series models using squared residual autocorrelations. J Time Ser Anal 4(4):269–273

    Article  Google Scholar 

  • Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A (2010) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158–162

    Article  Google Scholar 

  • Mohammed AA, Minhas R, Wu QJ, Sid-Ahmed MA (2011) Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recognit 44(10–11):2588–2597

    Article  Google Scholar 

  • Mugume I, Mesquita M, Bamutaze Y, Ntwali D, Basalirwa C, Waiswa D, Reuder J, Twinomuhangi R, Tumwine F, Ngailo TJ, Ogwang B (2018) Improving quantitative rainfall prediction using ensemble analogues in the tropics: case study of Uganda. Atmosphere 9(9):328

    Article  Google Scholar 

  • Ohba M, Sugimoto S (2019) Differences in climate change impacts between weather patterns: possible effects on spatial heterogeneous changes in future extreme rainfall. Clim Dyn 52:4177–4191

    Article  Google Scholar 

  • Ouyang T, Zha X, Qin L, Xiong Y, Xia T (2016) Wind power prediction method based on regime of switching kernel functions. J Wind Eng Ind Aerodyn 153:26–33

    Article  Google Scholar 

  • Ouyang T, He Y, Li H, Sun Z, Baek S (2019) Modeling and forecasting short-term power load with copula model and deep belief network. IEEE Trans Emerg Top Comput Intell 3(2):127–136

    Article  Google Scholar 

  • Pal S, Dutta S, Nasrin T et al (2020) Hurst exponent approach through rescaled range analysis to study the time series of summer monsoon rainfall over northeast India. Theor Appl Climatol 142:581–587

    Article  Google Scholar 

  • Pankratz A (1983) Forecasting with univariate Box–Jenkins method. Wiley, New York

    Book  Google Scholar 

  • Petković MD, Stanimirović PS (2011) Iterative method for computing the Moore–Penrose inverse based on Penrose equations. J Comput Appl Math 235(6):1604–1613

    Article  Google Scholar 

  • Ray SN, Chattopadhyay S (2021) Analyzing surface air temperature and rainfall in univariate framework, quantifying uncertainty through Shannon entropy and prediction through artificial neural network. Earth Sci Inf. https://doi.org/10.1007/s12145-020-00555-5

    Article  Google Scholar 

  • Ray S, Bose S, Chattopadhyay S (2021) A markov chain approach to the predictability of surface temperature over the northeastern part of India. Theor Appl Climatol 143:861–868. https://doi.org/10.1007/s00704-020-03458-z

    Article  Google Scholar 

  • Similä T, Tikka J (2005) Multiresponse sparse regression with application to multidimensional scaling. In: International conference on artificial neural networks. Springer, Berlin, pp 97–102

  • Sivapragasam C, Liong SY, Pasha MFK (2001) Rainfall and runoff forecasting with SSA-SVM approach. J Hydroinform 3(3):213–217

    Article  Google Scholar 

  • Suresh S, Babu RV, Kim HJ (2009) No-reference image quality assessment using modified extreme learning machine classifier. Appl Soft Comput 9(2):541–552

    Article  Google Scholar 

  • Tian X, Veldhuis M, Schleiss M et al (2019) Critical rainfall thresholds for urban pluvial flooding inferred from citizen observations. Sci Total Environ 689:258–268

    Article  Google Scholar 

  • Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239(1–4):132–147

    Article  Google Scholar 

  • Wang Y, Cheng C, Xie Y et al (2017) Increasing trends in rainfall–runoff erosivity in the source region of the Three Rivers, 1961–2012. Sci Total Environ 592:639–648

    Article  Google Scholar 

  • Wu J, Long J, Liu M (2015) Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing 148:136–142

    Article  Google Scholar 

  • Wu Z, Zhou Y, Wang H, Jiang Z (2020) Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse. Sci Total Environ 716:137077

    Article  Google Scholar 

  • Xu Q, Li H, He Y, Liu F, Peng D (2019) Comparison of data-driven models of loess landslide runout distance estimation. Bull Eng Geol Environ 78(2):1281–1294

    Article  Google Scholar 

  • Yen M, Lui D, Hsin Y, Lin C, Chen C (2019) Application of deep learning for the prediction of rainfall in southern Taiwan. Sci Rep 9:12774

    Article  Google Scholar 

Download references

Acknowledgements

We are immensely grateful to the suggestions and help from Andrés Felipe Alonso Rodriguez.

Author information

Authors and Affiliations

  1. School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, Sichuan, China

    Huajin Li & He Yang

  2. Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA, 52242, USA

    Yusen He

  3. State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection, Chengdu University of Technology, Chengdu, 610059, Sichuan, China

    Yong Wei & Jianqiang Xu

  4. Department of Natural Resources of Guizhou Province, Guiyang, 550004, Guizhou, China

    Songlin Li

Authors
  1. Huajin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yusen He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. He Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Songlin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jianqiang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yusen He.

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

Li, H., He, Y., Yang, H. et al. Rainfall prediction using optimally pruned extreme learning machines. Nat Hazards 108, 799–817 (2021). https://doi.org/10.1007/s11069-021-04706-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-021-04706-9

Keywords

Search

Navigation