Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals

References

• Abtew, W., Melesse, M.A., and Dessalegne, T., 2009. El Nino southern oscillation link to the Blue Nile River Basin hydrology. Hydrological Process, 23 (26), 3653–3660.
   Web of Science ®Google Scholar
• Abyaneh, H.Z., et al., 2011. Performance evaluation of ANN and ANFIS models for estimating garlic crop evapotranspiration. Journal of Irrigation and Drainage Engineering, 137 (5), 280–286. doi:10.1061/(ASCE)IR.1943-4774.0000298
   Web of Science ®Google Scholar
• Azadi, S. and Sepaskhah, A.R., 2012. Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks. Theoretical and Applied Climatology, 109 (1–2), 175–189. doi:10.1007/s00704-011-0575-9
   Web of Science ®Google Scholar
• Berg, N., et al., 2013. El Niño-Southern Oscillation impacts on winter winds over Southern California. Climate Dynamics, 40 (1–2), 109–121. doi:10.1007/s00382-012-1461-6
   Web of Science ®Google Scholar
• Cañón, J., González, J., and Valdés, J., 2007. Precipitation in the Colorado River Basin and its low frequency associations with PDO and ENSO signals. Journal of Hydrology, 333 (2–4), 252–264. doi:10.1016/j.jhydrol.2006.08.015
   Web of Science ®Google Scholar
• Chattopadhyay, S., 2007. Feed forward artificial neural network model to predict the average summer monsoon rainfall in India. Acta Geophysica, 55 (3), 369–382. doi:10.2478/s11600-007-0020-8
   Web of Science ®Google Scholar
• Chiu, S.L., 1994. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems, 2 (3), 267–278.
   Google Scholar
• Courville, T. and Thompson, B. 2001. Use of structure coefficients in published multiple regression articles: β is not enough. Educational and Psychological Measurement, 61 (2), 229–248. doi:10.1177/0013164401612006
   Google Scholar
• Dahamsheh, A. and Aksoy, H., 2009. Artificial neural network models for forecasting intermittent monthly precipitation in arid regions. Meteorological Applications, 16 (3), 325–337. doi:10.1002/met.127
   Web of Science ®Google Scholar
• Dastorani, M.T., et al., 2010. Application of ANN and ANFIS models on dryland precipitation prediction (Case study: Yazd in Central Iran). Journal of Applied Sciences, 10 (20), 2387–2394. doi:10.3923/jas.2010.2387.2394
   Google Scholar
• El-Shafie, A., Jaafer, O., and Seyed, A., 2011. Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia. International Journal of the Physical Sciences, 6 (12), 2875–2888.
   Google Scholar
• Fallah-Ghalhary, G.A., et al., 2010. Spring rainfall prediction based on remote linkage controlling using adaptive neuro-fuzzy inference system (ANFIS). Theoretical and Applied Climatology, 101 (1–2), 217–233. doi:10.1007/s00704-009-0194-x
   Web of Science ®Google Scholar
• Famini, G.R., Penski, C.A., and Wilson, L.Y., 1992. Using theoretical descriptors in quantitative structure activity relationships: some physicochemical properties. Journal of Physical Organic Chemistry, 5 (7), 395–408. doi:10.1002/poc.610050704
   Web of Science ®Google Scholar
• Faria, R., Duncan, J.C., and Brereton, R.G., 2007. Dynamic mechanical analysis and chemometrics for polymer identification. Polymer Testing, 26 (3), 402–412. doi:10.1016/j.polymertesting.2006.12.012
   Web of Science ®Google Scholar
• Farokhnia, A., Morid, S., and Byun, H.-R., 2011. Application of global SST and SLP data for drought forecasting on Tehran plain using data mining and ANFIS techniques. Theoretical and Applied Climatology, 104 (1–2), 71–81. doi:10.1007/s00704-010-0317-4
   Web of Science ®Google Scholar
• Firat, M. and Güngör, M., 2007. River flow estimation using adaptive neuro fuzzy inference system. Mathematics and Computers in Simulation, 75, 87–96. doi:10.1016/j.matcom.2006.09.003
   Web of Science ®Google Scholar
• Gámiz-Fortis, S.R., et al., 2010. Potential predictability of an Iberian river flow based on its relationship with previous winter global SST. Journal of Hydrology, 385 (1–4), 143–149. doi:10.1016/j.jhydrol.2010.02.010
   Web of Science ®Google Scholar
• Grantz, K., et al., 2005. A technique for incorporating large-scale climate information in basin-scale ensemble stream flow forecasts. Water Resources Research, 41 (10), 1–13. doi:10.1029/2004WR003467
   Web of Science ®Google Scholar
• Günaydın, O., 2009. Estimation of soil compaction parameters by using statistical analyses and artificial neural networks. Environmental Geology, 57 (1), 203–215. doi:10.1007/s00254-008-1300-6
   Web of Science ®Google Scholar
• Hsu, K.-L., Gupta, H.V., and Sorooshian, S., 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resources Research, 31 (10), 2517–2530. doi:10.1029/95WR01955
   Web of Science ®Google Scholar
• Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing, 70 (1–3), 489–501. doi:10.1016/j.neucom.2005.12.126
   Web of Science ®Google Scholar
• Jang, J.-S.R., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23 (3), 665–685. doi:10.1109/21.256541
   Web of Science ®Google Scholar
• Jang, J.-S.R. and Sun, C.-T., 1995. Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83 (3), 378–406. doi:10.1109/5.364486
   Web of Science ®Google Scholar
• Jeong, C.H., et al., 2012. Monthly precipitation forecasting with a neuro-fuzzy model. Water Resources Management, 26 (15), 4467–4483. doi:10.1007/s11269-012-0157-3
   Web of Science ®Google Scholar
• Jolliffe, I.T., Jones, B., and Morgan, B.J.T., 1980. Cluster analysis of the elderly at home: a case study. In: E. Diday et al., eds. Data analysis and informatics. Amsterdam: North-Holland, 745–757.
   Google Scholar
• Kaiser, H.F., 1970. A second generation little jiffy. Psychometrika, 35, 401–415. doi:10.1007/BF02291817
   Web of Science ®Google Scholar
• Kaiser, H.F., 1974. An index of factorial simplicity. Psychometrika, 39, 31–36. doi:10.1007/BF02291575
   Web of Science ®Google Scholar
• Kalra, A. and Ahmad, S., 2009. Using oceanic–atmospheric oscillations for long lead time streamflow forecasting. Water Resources Research, 45 (3), 1–18. doi:10.1029/2008WR006855
   Web of Science ®Google Scholar
• Kalra, A., et al.,2013. Using large-scale climatic patterns for improving long lead time streamflow forecasts for Gunnison and San Juan River Basins. Hydrological Processes, 27 (11), 1543–1559. doi:10.1002/hyp.9236
   Web of Science ®Google Scholar
• Karabork, M.C., Kahya, E., and Karaca, M., 2005. The influences of the Southern and North Atlantic Oscillations on climatic surface variables in Turkey. Hydrological Processes, 19 (6), 1185–1211. doi:10.1002/hyp.5560
   Web of Science ®Google Scholar
• Kim, T. and Valdés, J., 2003. Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering, 8 (6), 319–328. doi:10.1061/(ASCE)1084-0699(2003)8:6(319)
   Web of Science ®Google Scholar
• Kim, T.-W., Yoo, C., and Ahn, J.-H., 2008. Influence of climate variation on seasonal precipitation in the Colorado River Basin. Stochastic Environmental Research and Risk Assessment, 22 (3), 411–420. doi:10.1007/s00477-007-0126-1
   Web of Science ®Google Scholar
• Kisi, O. and Shiri, J., 2011. Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resources Management, 25 (13), 3135–3152. doi:10.1007/s11269-011-9849-3
   Web of Science ®Google Scholar
• Kumar, D.N., Reddy, M.J., and Maity, R., 2007. Regional rainfall forecasting using large scale climate teleconnections and artificial intelligence techniques. Journal of Intelligent Systems, 16 (4), 307–322.
   Google Scholar
• Kurtulus, B. and Razack, M., 2010. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: artificial neural network and neuro-fuzzy. Journal of Hydrology, 381 (1–2), 101–111. doi:10.1016/j.jhydrol.2009.11.029
   Web of Science ®Google Scholar
• Quraishi, M.Z. and Mouazen, A.M., 2013. Development of a methodology for in situ assessment of topsoil dry bulk density. Soil and Tillage Research, 126, 229–237. doi:10.1016/j.still.2012.08.009
   Web of Science ®Google Scholar
• Ringnér, M., 2008. What is principal component analysis? Nature Biotechnology, 26 (3), 303–304. doi:10.1038/nbt0308-303
   PubMed Web of Science ®Google Scholar
• Rumelhart, D.E., Hinton, G.E., and Williams, R.J., 1986. Learning representations by back-propagating errors. Nature, 323, 533–536. doi:10.1038/323533a0
   Web of Science ®Google Scholar
• Sanikhani, H. and Kisi, O., 2012. River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches. Water Resources Management, 26 (6), 1715–1729. doi:10.1007/s11269-012-9982-7
   Web of Science ®Google Scholar
• Seleshi, Y. and Zanke, U., 2004. Recent changes in rainfall and rainy days in Ethiopia. International Journal of Climatology, 24 (8), 973–983. doi:10.1002/joc.1052
   Web of Science ®Google Scholar
• Shrestha, S. and Kazama, F., 2007. Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environmental Modelling and Software, 22 (4), 464–475. doi:10.1016/j.envsoft.2006.02.001
   Web of Science ®Google Scholar
• Sigaroodi, S.K., et al., 2014. Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharlu Basin in Iran. Hydrology and Earth System Sciences, 18 (5), 1995–2006. doi:10.5194/hess-18-1995-2014.
   Web of Science ®Google Scholar
• Takagi, T. and Sugeno, M., 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15 (1), 116–132. doi:10.1109/TSMC.1985.6313399
   Web of Science ®Google Scholar
• Tomassetti, B., Verdecchia, M., and Giorgi, F., 2009. NN5: A neural network based approach for the downscaling of precipitation fields – model description and preliminary results. Journal of Hydrology, 367 (1–2), 14–26. doi:10.1016/j.jhydrol.2008.12.017
   Web of Science ®Google Scholar
• Trafalis, T.B., et al., 2002. Data mining techniques for improved WSR-88D rainfall estimation. Computers & Industrial Engineering, 43 (4), 775–786. doi:10.1016/S0360-8352(02)00139-0
   Web of Science ®Google Scholar
• Turkes, M. and Elart, E., 2003. Precipitation changes and variability in Turkey linked to the North Atlantic oscillation during the period 1930-2000. International Journal of Climatology, 23 (14), 1771–1796. doi:10.1002/joc.962
   Web of Science ®Google Scholar
• Valverde Ramírez, M.C., De Campos Velho Maria, H.F., and Ferreira, N.J., 2005. Artificial neural network technique for rainfall forecasting applied to the São Paulo region. Journal of Hydrology, 301 (1–4), 146–162. doi:10.1016/j.jhydrol.2004.06.028
   Web of Science ®Google Scholar
• Wallace, J.M., 2000. North Atlantic oscillation/annular mode: two paradigms -one phenomenon. Quarterly Journal of the Royal Meteorological Society, 126 (564), 791–805. doi:10.1256/smsqj.56401
   Web of Science ®Google Scholar
• Zientek, L.R., Capraro, M.M., and Capraro, R.M., 2008. Reporting practices in quantitative teacher education research: one look at the evidence cited in the AERA panel report. Educational Researcher, 37 (4), 208–216. doi:10.3102/0013189X08319762
   Google Scholar