Hybrid ANFIS-PSO Model for Monthly Precipitation Forecasting

1. Choubin, B., Malekian, A., Samadi, S., Khalighi-Sigaroodi, S., Sajedi-Hosseini, F.: An ensemble forecast of semi-arid rainfall using large-scale climate predictors. Meteorol. Appl. 24(3), 376–386 (2017)

   Article  Google Scholar 

2. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39–43). IEEE (1995)

   Google Scholar 

3. Jalalkamali, A.: Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters. Earth Sci. Inf. 8(4), 885–894 (2015)

   Article  Google Scholar 

4. Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

   Article  Google Scholar 

5. Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Trans. Autom. Control 42(10), 1482–1484 (1997)

   Article  Google Scholar 

6. Jeong, C., Shin, J.Y., Kim, T., Heo, J.H.: Monthly precipitation forecasting with a neuro-fuzzy model. Water Resour. Manage 26(15), 4467–4483 (2012)

   Article  Google Scholar 

7. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol. 4, pp. 1942–1948. (1995)

   Google Scholar 

8. Kisi, O., Sanikhani, H.: Prediction of long-term monthly precipitation using several soft computing methods without climatic data. Int. J. Climatol. 35(14), 4139–4150 (2015)

   Article  Google Scholar 

9. Liu, Z., Zhang, D., Peng, W.: A Novel ANFIS-PSO Network for forecasting oil flocculated asphaltene weight percentage at wide range of operation conditions. Pet. Sci. Technol. 36(14), 1044–1050 (2018)

   Article  Google Scholar 

10. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975)

    Article  Google Scholar 

11. Mohammadi, K., Shamshirband, S., Petković, D., Yee, L., Mansor, Z.: Using ANFIS for selection of more relevant parameters to predict dew point temperature. Appl. Therm. Eng. 96, 311–319 (2016)

    Article  Google Scholar 

12. Mohammadi, K., Shamshirband, S., Tong, C.W., Alam, K.A., Petković, D.: Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year. Energy Convers. Manage. 93, 406–413 (2015)

    Article  Google Scholar 

13. Mohanta, N.R., Patel, N., Beck, K., Samantaray, S., Sahoo, A.: Efficiency of river flow prediction in river using Wavelet-CANFIS: a case study. In: Intelligent Data Engineering and Analytics (pp. 435–443). Springer, Singapore (2021a)

    Google Scholar 

14. Mohanta, N.R., Biswal, P., Kumari, S.S., Samantaray, S., Sahoo, A.: Estimation of sediment load using adaptive neuro-fuzzy inference system at indus river basin, India. In: Intelligent Data Engineering and Analytics (pp. 427–434). Springer, Singapore (2021b)

    Google Scholar 

15. Nguyen, Q.H., Ly, H.B., Le, T.T., Nguyen, T.A., Phan, V.H., Tran, V.Q., Pham, B.T.: Parametric investigation of particle swarm optimization to improve the performance of the adaptive neuro-fuzzy inference system in determining the buckling capacity of circular opening steel beams. Materials 13(10), 2210 (2020)

    Article  Google Scholar 

16. Sahoo, A., Samantaray, S., Bankuru, S., Ghose, D.K.: Prediction of flood using adaptive neuro-fuzzy inference systems: a case study. In: Smart Intelligent Computing and Applications (pp. 733–739). Springer, Singapore (2020)

    Google Scholar 

17. Samantaray, S., Sahoo, A., Ghose, D.K.: Infiltration loss affects toward groundwater fluctuation through CANFIS in arid watershed: a case study. In: Smart Intelligent Computing and Applications (pp. 781–789). Springer, Singapore (2020a)

    Google Scholar 

18. Samantaray, S., Sahoo, A., Ghose, D.K.: Prediction of sedimentation in an arid watershed using BPNN and ANFIS. In: ICT Analysis and Applications (pp. 295–302). Springer, Singapore (2020b)

    Google Scholar 

19. Shamshirband, S., Gocić, M., Petković, D., Saboohi, H., Herawan, T., Kiah, M.L.M., Akib, S.: Soft-computing methodologies for precipitation estimation: a case study. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 8(3), 1353–1358 (2014)

    Article  Google Scholar 

20. Sridharam, S., Sahoo, A., Samantaray, S., Ghose, D.K.: Assessment of flow discharge in a river basin through CFBPNN, LRNN and CANFIS. In: Communication Software and Networks (pp. 765–773). Springer, Singapore (2021)

    Google Scholar 

21. Yaseen, Z.M., Ebtehaj, I., Kim, S., Sanikhani, H., Asadi, H., Ghareb, M.I., Bonakdari, H., Wan Mohtar, W.H.M., Al-Ansari, N., Shahid, S. Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. Water 11(3), 502 (2019)

    Google Scholar 

22. Zanganeh, M.: Improvement of the ANFIS-based wave predictor models by the Particle Swarm Optimization. J. Ocean Eng. Sci. 5(1), 84–99 (2020)

    Article  Google Scholar 

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