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Comparative Analysis of Artificial Intelligence Based Methods for Prediction of Precipitation. Case Study: North Cyprus

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13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018 (ICAFS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 896))

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Abstract

Prediction of precipitation is important for design, management of water resources systems, planning, flood predicting and hydrological events. This study aimed to compare the performance of three different “Artificial Intelligence (AI)” techniques which are “Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS) and Least Square Support Vector Machine (LSSVM)” to estimate monthly rainfall in Kyrenia Station of Turkish Republic of Northern Cyprus (TRNC). The monthly data covering ten years’ precipitation were used for the predictions. The comparative results showed that the LSSVM model can cause a bit more reliable performance in regard to ANN and ANFIS.

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References

  1. Akrami, S.A., Nourani, V., Hakim, S.J.S.: Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang Gates Dam. Water Resour. Manag. 28(10), 2999–3018 (2014)

    Article  Google Scholar 

  2. Abbot, J., Marohasy, J.: Application of artificial neural networks to rainfall forecasting in Queensland, Australia. Adv. Atmos. Sci. 29(4), 717–730 (2012)

    Article  Google Scholar 

  3. Bisht, D., Joshi, M.C., Mehta, A.: Prediction of monthly rainfall of Nainital region using artificial neural network and support vector machine. Int. J. Adv. Res. Innov. Ideas Educ. 1(3), 2395–4396 (2015)

    Google Scholar 

  4. Devi, S.R., Arulmozhivarman, P., Venkatesh, C.: ANN based rainfall prediction - a tool for developing a landslide early warning system. In: Advancing Culture of Living with Landslides - Workshop on World Landslide Forum, pp. 175–182 (2017)

    Google Scholar 

  5. Guo, J., Zhou, J., Qin, H., Zou, Q., Li, Q.: Monthly streamflow forecasting based on improved support vector machine model. Expert Syst. Appl. 38(10), 13073–13081 (2011)

    Article  Google Scholar 

  6. Guhathakurta, P.: Long lead monsoon rainfall prediction for meteorological sub-divisions of India using deterministic artificial neural network model. Meteorol. Atmos. Phys. 101(2), 93–108 (2008)

    Article  Google Scholar 

  7. Hung, N.Q., Babel, M.S., Weesakul, S., Tripathi, N.K.: An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol. Earth Syst. Sci. 13, 1413–1425 (2009)

    Article  Google Scholar 

  8. Khalili, N., Khodashenas, S.R., Davary, K., Mousavi, B., Karimaldini, F.: Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study. Arab. J. Geosci. 9, 624 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Kumar, M., Kar, I.N.: Non-linear HVAC computations using least square support vector machines. Energy Convers. Manag. 50(6), 1411–1418 (2009)

    Article  Google Scholar 

  11. Lu, K., Wang, L.: A novel nonlinear combination model based on support vector machine for rainfall prediction. In: Fourth International Joint Conference on Computational Sciences and Optimization, pp. 1343–1347. Computational Sciences and Optimization, China (2011)

    Google Scholar 

  12. Mokhtarzad, M., Eskandari, F., Vanjani, N.J., Arabasadi, A.: Drought forecasting by ANN, ANFIS and SVM and comparison of the models. Environ. Earth Sci. 76(21), 729 (2017)

    Article  Google Scholar 

  13. Murphy, R.A.: A predictive model using the Markov Property. http://arxiv.org/abs/1601.01700v1. Accessed 1 Nov 2016

  14. Nourani, V.: An emotional ANN (EANN) approach to modelling rainfall-runoff process. J. Hydrol. 544, 267–277 (2017)

    Article  Google Scholar 

  15. Nourani, V., Andalib, G.: Daily and monthly suspended sediment load predictions using wavelet-based AI approaches. J. Mt. Sci. 12(1), 85–100 (2015)

    Article  Google Scholar 

  16. Nourani, V., Babakhani, A.: Integration of artificial neural networks with radial basis function interpolation in earthfill dam seepage modelling. J. Comput. Civ. Eng. 27(2), 183–195 (2012)

    Article  Google Scholar 

  17. Nourani, V., Komasi, M.: A geomorphology-based ANFIS model for multi-station modelling of rainfall–runoff process. J. Hydrol. 490, 41–55 (2013)

    Article  Google Scholar 

  18. Nourani, V., Mogaddam, A.A., Nadiri, A.O.: An ANN based model for spatiotemporal groundwater level forecasting. Hydrol. Process. 22(26), 5054–5066 (2008)

    Article  Google Scholar 

  19. Nourani, V., Parhizkar, M.: Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall–runoff modelling. J. Hydroinform. 15(3), 829–848 (2013)

    Article  Google Scholar 

  20. Nourani, V., Sharghi, E., Aminfar, M.H.: Integrated ANN model for earthfill dams seepage analysis: Sattarkhan Dam in Iran. Artif. Intell. Res. 1(2), 22–37 (2012)

    Article  Google Scholar 

  21. Ortiz-Garcia, E.G., Salcedo-Sanz, S., Casanova-Mateo, C.: Accurate precipitation prediction with support vector classifiers: a study including novel predictive variables and observational data. Atmos. Res. 139, 128–136 (2014)

    Article  Google Scholar 

  22. Sharifi, S.S., Delirhasannia, R., Nourani, V., Sadraddini, A.A., Ghorbani, A.: Using ANNs and ANFIS for modelling and sensitivity analysis of effective rainfall. In: Recent Advances in Continuum Mechanics, Hydrology and Ecology. ISBN 978-960-474-313-1

    Google Scholar 

  23. Cao, S.-G., Liu, Y.B., Wang, Y.P.: A forecasting and forewarning model for methane hazard in working face of coal mine based on LSSVM. J. China Univ. Min. Technol. 18(2), 172–176 (2008)

    Article  Google Scholar 

  24. Singh, V.K., Kumar, P., Singh, B.P., Malik, A.: A comparative study of adaptive neuro fuzzy inference system (ANFIS) and multiple linear regression (MLR) for rainfall-runoff modelling. Int. J. Sci. Nat. 7(4), 714–723 (2016)

    Google Scholar 

  25. Sojitra, M.A., Purohit, R.C., Pandya, P.A.: Comparative study of daily rainfall forecasting models using ANFIS. Curr. World Environ. 10(2), 529–536 (2015)

    Article  Google Scholar 

  26. Solgi, A., Nourani, V., Pourhaghi, A.: Forecasting daily precipitation using hybrid model of wavelet-artificial neural network and comparison with adaptive neurofuzzy inference system. Adv. Civ. Eng. 2014(3), 1–12 (2014)

    Google Scholar 

  27. Suykens, J.A.K., Vandewalle, J.: Least square support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)

    Article  Google Scholar 

  28. Ye, J., Xiong, T.: SVM versus least squares SVM. In: Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, no. 2, pp. 644–651 (2007)

    Google Scholar 

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

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Near East University, POBOX 99138, Nicosia, Mersin 10, TRNC, Turkey

    Selin Uzelaltinbulat

  2. Department of Electrical and Electronic Engineering, Faculty of Engineering, Near East University, POBOX 99138, Nicosia, Mersin 10, TRNC, Turkey

    Fahreddin Sadikoglu

  3. Department of Civil Engineering, Faculty of Engineering, Near East University, POBOX 99138, Nicosia, Mersin 10, TRNC, Turkey

    Vahid Nourani

  4. Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    Vahid Nourani

Authors
  1. Selin Uzelaltinbulat
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  2. Fahreddin Sadikoglu
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  3. Vahid Nourani
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Corresponding author

Correspondence to Selin Uzelaltinbulat .

Editor information

Editors and Affiliations

  1. Joint MBA Program, Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Rafik A. Aliev

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  4. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

    Mo. Jamshidi

  5. Department of Mechatronics, Near East University, Nicosia TRNC, Turkey

    Fahreddin M. Sadikoglu

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Uzelaltinbulat, S., Sadikoglu, F., Nourani, V. (2019). Comparative Analysis of Artificial Intelligence Based Methods for Prediction of Precipitation. Case Study: North Cyprus. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_11

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