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Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform

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Abstract

Precipitation prediction is of dispensable importance in many hydrological applications. In this study, monthly precipitation data sets from Serbia for the period 1946–2012 were used to estimate precipitation. To fulfil this objective, three mathematical techniques named artificial neural network (ANN), genetic programming (GP) and support vector machine with wavelet transform algorithm (WT-SVM) were applied. The mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R2) were used to evaluate the performance of the WT-SVM, GP and ANN models. The achieved results demonstrate that the WT-SVM outperforms the GP and ANN models for estimating monthly precipitation.

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References

  • Abbot J, Marohasy J (2014) Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmos Res 138:166–178

    Article  Google Scholar 

  • Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407(1):28–40

    Article  Google Scholar 

  • Chau K, Wu C (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinf 12(4):458–473

    Article  Google Scholar 

  • Dileep AD, Sekhar CC (2014) Class-specific GMM based intermediate matching kernel for classification of varying length patterns of long duration speech using support vector machines. Speech Comm 57:126–143

    Article  Google Scholar 

  • Du S, Huang D, Lv J (2013) Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines. Comput Ind Eng 66(4):683–695

    Article  Google Scholar 

  • Feng Q, Wen X, Li J (2015) Wavelet analysis-support vector machine coupled models for monthly rainfall forecasting in arid regions. Water Resour Manag 29:1049–1065

    Article  Google Scholar 

  • Freiwan M, Cigizoglu HK (2005) Prediction of total monthly rainfall in Jordan using feed forward backpropagation method. Fresenius Environ Bull 14(2):142–151

    Google Scholar 

  • Gocic M, Trajkovic S (2014a) Spatiotemporal characteristics of drought in Serbia. J Hydrol 510:110–123

    Article  Google Scholar 

  • Gocic M, Trajkovic S (2014b) Spatio-temporal patterns of precipitation in Serbia. Theor Appl Climatol 117(3–4):419–431

    Article  Google Scholar 

  • Guo J, Yi P, Wang R, Ye Q, Zhao C (2014) Feature selection for least squares projection twin support vector machine. Neurocomputing 144:174–183

    Article  Google Scholar 

  • Harris T (2015) Credit scoring using the clustered support vector machine. Expert Syst Appl 42(2):741–750

    Article  Google Scholar 

  • Jawerth B, Sweldens W (1994) An overview of wavelet based multiresolution analyses. SIAM Rev 36(3):377–412

    Article  Google Scholar 

  • Kalteh AM (2013) Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Comput Geosci 54:1–8

    Article  Google Scholar 

  • Kalteh AM (2015) Wavelet genetic algorithm-support vector regression (Wavelet GA-SVR) for monthly flow forecasting. Water Resour Manag 29(4):1283–1293

    Article  Google Scholar 

  • Kisi O, Cimen M (2012) Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng Appl Artif Intel 25(4):783–792

    Article  Google Scholar 

  • Liang C-W, Juang C-F (2015) Moving object classification using local shape and HOG features in wavelet-transformed space with hierarchical SVMclassifiers. Appl Soft Comput 28:483–497

    Article  Google Scholar 

  • Lu C-J, Shao YE, Li P-H (2011) Mixture control chart patterns recognition using independent component analysis and support vector machine. Neurocomputing 74(11):1908–1914

    Article  Google Scholar 

  • Mekanik F, Imteaz MA, Gato-Trinidad S, Elmahdi A (2013) Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes. J Hydrol 503:11–21

    Article  Google Scholar 

  • Mohammadi K, Shamshirband S, Tong CW, Arif M, Petkovic D, Ch S (2015) A new hybrid support vector machine-wavelet transform approach for estimation of horizontal global solar radiation. Energy Convers Manag 92:162–171

    Article  Google Scholar 

  • Moustris KP, Larissi IK, Nastos PT, Paliatsos AG (2011) Precipitation forecast using artificial neural networks in specific regions of Greece. Water Resour Manag 25(8):1979–1993

    Article  Google Scholar 

  • Nasseri M, Asghari K, Abedini MJ (2008) Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Syst Appl 35(3):1415–1421

    Article  Google Scholar 

  • Nourani V, Hosseini Baghanam A, Adamowski J, Gebremichael M (2014) Using self-organizing maps and wavelet transforms for space-time pre-processing of satellite precipitation and runoff data in neural network based rainfall-runoff modeling. J Hydrol 476:228–243

    Article  Google Scholar 

  • Ortiz-García EG, Salcedo-Sanz S, Casanova-Mateo C (2014) Accurate precipitation prediction with support vector classifiers: A study including novel predictive variables and observational data. Atmos Res 139:128–136

    Article  Google Scholar 

  • Peng Z, Chu F (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18(2):199–221

    Article  Google Scholar 

  • Sahay RR, Srivastava A (2014) Predicting monsoon floods in rivers embedding wavelet transform, genetic algorithm and neural network. Water Resour Manag 28:301–317

    Article  Google Scholar 

  • Sánchez-Monedero J, Salcedo-Sanz S, Gutiérrez PA, Casanova-Mateo C, Hervás-Martínez C (2014) Simultaneous modelling of rainfall occurrence and amount using a hierarchical nominal-ordinal support vector classifier. Eng Appl Artif Intel 34:199–207

    Article  Google Scholar 

  • Sedki A, Ouazar D, El Mazoudi E (2009) Evolving neural network using real coded genetic algorithm for daily rainfall–runoff forecasting. Expert Syst Appl 36(3):4523–4527

    Article  Google Scholar 

  • Shamshirband S, Gocić M, Petković D, Saboohi H, Herawan T, Mat Kiah ML, Akib S (2014) Soft-Computing Methodologies for Precipitation Estimation: A Case Study. IEEE J Sel Top Appl Earth Obs Remote Sens. doi:10.1109/JSTARS.2014.2364075

    Google Scholar 

  • Shiau JT, Huang CY (2014) Detecting multi-purpose reservoir operation induced time-frequency alteration using wavelet transform. Water Resour Manag 28:3577–3590

    Article  Google Scholar 

  • Sun Y, Leng B, Guan W (2015) A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing 166:109–121

    Article  Google Scholar 

  • Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330(3-4):621–640

    Article  Google Scholar 

  • Valverde Ramírez MC, Campos Velho HF, Ferreira NJ (2005) Artificial neural network technique for rainfall forecasting applied to the São Paulo region. J Hydrol 301(1–4):146–162

    Article  Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  • Vapnik VN (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Venkata Ramana R, Krishna B, Kumar SR, Pandey NG (2013) Monthly rainfall prediction using wavelet neural network analysis. Water Resour Manag 27:3697–3711

    Article  Google Scholar 

  • Wang W, Li Y (2011) Wavelet transform method for synthetic generation of daily stream flow. Water Resour Manag 25:41–57

    Article  Google Scholar 

  • Wu CL, Chau KW, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389(1–2):146–167

    Article  Google Scholar 

  • Yang X, Tan L, He L (2014) A robust least squares support vector machine for regression and classification with noise. Neurocomputing 140:41–52

    Article  Google Scholar 

  • Yong DD, Bhowmik S, Magnago F (2015) An effective power quality classifier using wavelet transform and support vector machines. Expert Syst Appl 42(15–16):6075–6081

    Article  Google Scholar 

Download references

Acknowledgments

This work is funded by the Malaysian Ministry of Higher Education under the University of Malaya High Impact Research Grant UM.C/625/1/HIR/MoHE/FCSIT/17, the Ministry of Education, Science and Technological Development, Republic of Serbia (Grant No. TR37003) and the ICT COST Action IC1408 Computationally-intensive methods for the robust analysis of non-standard data (CRoNoS).

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Authors and Affiliations

  1. Department of Computer Science, College of Computer Science and Information Technology, Albaha University, Albaha, 65431, Kingdom of Saudi Arabia

    Mohamed Shenify

  2. Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Amir Seyed Danesh

  3. Faculty of Civil Engineering and Architecture, University of Niš, Aleksandra Medvedeva 14, 18000, Niš, Serbia

    Milan Gocić

  4. Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia

    Ros Surya Taher

  5. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Ainuddin Wahid Abdul Wahab, Abdullah Gani & Shahaboddin Shamshirband

  6. Faculty of Mechanical Engineering, University of Niš, Aleksandra Medvedeva 14, 18000, Niš, Serbia

    Dalibor Petković

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  1. Mohamed Shenify
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  2. Amir Seyed Danesh
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  3. Milan Gocić
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  4. Ros Surya Taher
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  5. Ainuddin Wahid Abdul Wahab
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  6. Abdullah Gani
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  7. Shahaboddin Shamshirband
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  8. Dalibor Petković
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Correspondence to Shahaboddin Shamshirband.

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Shenify, M., Danesh, A.S., Gocić, M. et al. Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform. Water Resour Manage 30, 641–652 (2016). https://doi.org/10.1007/s11269-015-1182-9

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  • DOI: https://doi.org/10.1007/s11269-015-1182-9

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