Daily rainfall estimates considering seasonality from a MODWT-ANN hybrid model

Addison, P.S., Murray, K.B., Watson, J.N., 2001. Wavelet transform analysis of open channel wake flows. Journal of Engineering Mechanics, 127, 58–70.10.1061/(ASCE)0733-9399(2001)127:1(58)Search in Google Scholar

Bašta, M., 2014. Additive decomposition and boundary conditions in wavelet-based forecasting approaches. Acta Oeconomica Pragensia, 2014, 48–70.10.18267/j.aop.431Search in Google Scholar

Bonal, D., Burban, B., Stahl, C., Wagner, F., Hérault, B., 2016. The response of tropical rainforests to drought - lessons from recent research and future prospects. Annals of Forest Science, 73, 27–44. DOI: 10.1007/s13595-015-0522-510.1007/s13595-015-0522-5481088827069374Search in Google Scholar

Chai, T., Draxler, R.R., 2014. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7, 1247–1250. DOI: 10.5194/gmd-7-1247-201410.5194/gmd-7-1247-2014Search in Google Scholar

Chau, K.W., Wu, C.L., 2010. A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. Journal of Hydroinformatics, 12, 458–473. DOI: 10.2166/hydro.2010.03210.2166/hydro.2010.032Search in Google Scholar

Cuo, L., Pagano, T.C., Wang, Q.J., 2011. A review of quantitative precipitation forecasts and their use in short-to-medium streamflow forecasting. Journal of Hydrometeorology, 12, 713–728.10.1175/2011JHM1347.1Search in Google Scholar

Daubechies, I., 1992. Ten Lectures on Wavelet. Society for Industrial and Applied Mathematics, Philadelphia. https://doi.org/10.1137/1.978161197010410.1137/1.9781611970104Search in Google Scholar

dos Santos, T.S., Mendes, D., Torres, R.R., 2016. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America. Nonlinear Processes in Geophysics, 23, 13–20. DOI: 10.5194/npg-23-13-201610.5194/npg-23-13-2016Search in Google Scholar

Du, K., Zhao, Y., Lei, J., 2017. The incorrect usage of singular spectral analysis and discrete wavelet transform in hybrid models to predict hydrological time series. J. Hydrol., 552, 44–51. DOI: 10.1016/j.jhydrol.2017.06.01910.1016/j.jhydrol.2017.06.019Search in Google Scholar

Espinoza, J.C., Segura, H., Ronchail, J., Drapeau, G., Gutierrez-Cori, O., 2016. Evolution of wet-day and dry-day frequency in the western Amazon basin: Relationship with atmospheric circulation and impacts on vegetation. Water Resources Research, 52, 8546–8560. https://doi.org/10.1002/2016WR01930510.1002/2016WR019305Search in Google Scholar

Fahimi, F., Yaseen, Z.M., El-shafie, A., 2017. Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theoretical and Applied Climatology, 128, 875–903. https://doi.org/10.1007/s00704-016-1735-810.1007/s00704-016-1735-8Search in Google Scholar

Falck, A.S., Maggioni, V., Tomasella, J., Vila, D.A., Diniz, F.L., 2015. Propagation of satellite precipitation uncertainties through a distributed hydrologic model: A case study in the Tocantins–Araguaia basin in Brazil. Journal of Hydrology, 527, 943–957. http://dx.doi.org/10.1016/j.jhydrol.2015.05.04210.1016/j.jhydrol.2015.05.042Search in Google Scholar

Frumau, K.A., Bruijnzeel, L.A., Tobón, C., 2011. Precipitation measurement and derivation of precipitation inclination in a windy mountainous area in northern Costa Rica. Hydrological Processes, 25, 499–509. https://doi.org/10.1002/hyp.786010.1002/hyp.7860Search in Google Scholar

Germano, M.F., Vitorino, M.I., Cohen, J.C.P., Costa, G.B., Souto, J.I.D.O., Rebelo, M.T.C., de Sousa, A.M.L., 2017. Analysis of the breeze circulations in Eastern Amazon: an observational study. Atmospheric Science Letters, 18, 67–75. https://doi.org/10.1002/asl.72610.1002/asl.726Search in Google Scholar

Gloor, M.R.J.W., Brienen, R.J., Galbraith, D., Feldpausch, T.R., Schöngart, J., Guyot, J.L., Phillips, O.L., 2013. Intensification of the Amazon hydrological cycle over the last two decades. Geophysical Research Letters, 40, 1729–1733. https://doi.org/10.1002/grl.5037710.1002/grl.50377Search in Google Scholar

Gnanadesikan, R., 2011. Methods for Statistical Data Analysis of Multivariate Observations. John Wiley & Sons. DOI:10.1002/978111803267110.1002/9781118032671Search in Google Scholar

Golding, B.W., 2014. Regional prediction models. In: North, G., Pyle, J., Zhang, F. (Eds.): Encyclopedia of Atmospheric Sciences. 2nd Edition. Academic Press, p. 2008.Search in Google Scholar

Gomes, E.P., Blanco, C.J.C., Pessoa, F.C.L., 2018. Regionalization of precipitation with determination of homogeneous regions via fuzzy c-means. Revista Brasileira de Recursos Hídricos, 23. https://doi.org/10.1590/2318-0331.23182018007910.1590/2318-0331.231820180079Search in Google Scholar

Guimarães Santos, C.A., Silva, G.B.L.D., 2014. Daily stream-flow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sciences Journal, 59, 312–324. http://dx.doi.org/10.1080/02626667.2013.80094410.1080/02626667.2013.800944Search in Google Scholar

Gupta, A., Kamble, T., Machiwal, D., 2017. Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India. Environmental Earth Sciences, 76, 512. https://doi.org/10.1007/s12665-017-6814-310.1007/s12665-017-6814-3Search in Google Scholar

He, X., Guan, H., Qin, J., 2015. A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall. Journal of Hydrology, 527, 88–100. http://dx.doi.org/10.1016/j.jhydrol.2015.04.0470022.Search in Google Scholar

Hellassa, S., Souag-Gamane, D., 2019. Improving a stochastic multi-site generation model of daily rainfall using discrete wavelet de-noising: a case study to a semi-arid region. Arabian Journal of Geosciences, 12, 53. https://doi.org/10.1007/s12517-018-4168-010.1007/s12517-018-4168-0Search in Google Scholar

Holdefer, A.E., Severo, D.L., 2015. Análise por ondaletas sobre níveis de rios submetidos à influência de maré. Revista Brasileira de Recursos Hídricos, 20, 192–201. DOI: 10.21168/rbrh.v20n1.p192-20110.21168/rbrh.v20n1.p192-201Search in Google Scholar

IBGE – INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA. Cobertura do uso da terra do Brasil (Land use coverage in Brazil). Rio de Janeiro: IBGE, 2014. Available from: https://www.ibge.gov.br/geocienciasnovoportal/informacoes-ambientais/cobertura-e-uso-da-terra (accessed in 13 Sept. 2017)Search in Google Scholar

Kisi, O., Cimen, M., 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399, 132–140.10.1016/j.jhydrol.2010.12.041Search in Google Scholar

Kisi, O., Shiri, J., 2011. Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resources Management, 25, 3135–3152. https://doi.org/10.1007/s11269-011-9849-310.1007/s11269-011-9849-3Search in Google Scholar

Kuo, C.C., Gan, T.Y., Yu, P.-S., 2010. Wavelet analysis on the variability, teleconnectivity, and predictability of the seasonal rainfall of Taiwan. Monthly Weather Review, 138, 162–175.10.1175/2009MWR2718.1Search in Google Scholar

Lang, K.J., Hinton, G.E., 1988. The development of the time-delay neural network architecture for speech recognition. Technical Report CMU-CS-88-152.Search in Google Scholar

Latrubesse, E.M., Arima, E.Y., Dunne, T., Park, E., Baker, V.R., d’Horta, F.M., Ribas, C.C., 2017. Damming the rivers of the Amazon basin. Nature, 546, 363–369. https://doi.org/10.1038/nature2233310.1038/nature22333Search in Google Scholar

Levy, M.C., Cohn, A., Lopes, A.V., Thompson, S.E., 2017. Addressing rainfall data selection uncertainty using connections between rainfall and streamflow. Scientific Reports, 7, 219. DOI: 10.1038/s41598-017-00128-510.1038/s41598-017-00128-5Search in Google Scholar

Maheswaran, R., Khosa, R., 2012. Comparative study of different wavelets for hydrologic forecasting. Computers & Geosciences, 46, 284–295. https://doi.org/10.1016/j.cageo.2011.12.01510.1016/j.cageo.2011.12.015Search in Google Scholar

Mallat, S., 2009. A Wavelet Tour of Signal Processing. Academic Press, 832 p. https://doi.org/10.1016/B978-0-12-374370-1.X0001-8.10.1016/B978-0-12-374370-1.X0001-8Search in Google Scholar

Mehr, A.D., Kahya, E., Bagheri, F., Deliktas, E., 2014. Successive-station monthly streamflow prediction using neurowavelet technique. Earth Science Informatics, 7, 217–229. DOI: 10.1007/s12145-013-0141-310.1007/s12145-013-0141-3Search in Google Scholar

Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I – A discussion of principles. Journal of Hydrology, 10, 282–290. http://doi.org/10.1016/0022-1694(70)90255-610.1016/0022-1694(70)90255-6Search in Google Scholar

Nerantzaki, S.D., Papalexiou, S.M., 2019. Tails of extremes: Advancing a graphical method and harnessing big data to assess precipitation extremes. Advances in Water Resources, 134, Article Number: 103448.10.1016/j.advwatres.2019.103448Search in Google Scholar

Nourani, V., Baghanam, A.H., Adamowski, J., Kisi, O., 2014. Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, 358–377. https://doi.org/10.1016/j.jhydrol.2014.03.05710.1016/j.jhydrol.2014.03.057Search in Google Scholar

Nourani, V., Andalib, G., Sadikoglu, F., 2017. Multi-station streamflow forecasting using wavelet denoising and artificial intelligence models. Procedia Computer Science, 120, 617–624. DOI: 10.1016/j.procs.2017.11.28710.1016/j.procs.2017.11.287Search in Google Scholar

Oliveira-Junior, J.F.D., Xavier, F.M.G., Teodoro, P.E., Gois, G.D., Delgado, R.C., 2017. Cluster analysis identified rainfall homogeneous regions in Tocantins State, Brazil. Bioscience Journal, 33, 333–340. https://doi.org/10.14393/BJ-v33n2-3273910.14393/BJ-v33n2-32739Search in Google Scholar

Osborn, T.J., Wallace, C.J., Harris, I.C., Melvin, T.M., 2016. Pattern scaling using ClimGen: monthly-resolution future climate scenarios including changes in the variability of precipitation. Climatic Change, 134, 353–369. https://doi.org/10.1007/s10584-015-1509-910.1007/s10584-015-1509-9Search in Google Scholar

Partal, T., Kişi, Ö., 2007. Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology, 342, 199–212. https://doi.org/10.1016/j.jhydrol.2007.05.02610.1016/j.jhydrol.2007.05.026Search in Google Scholar

Partal, T., Cigizoglu, H.K., 2009. Prediction of daily precipitation using wavelet—neural networks. Hydrological Sciences Journal, 54:2, 234–246, DOI: 10.1623/hysj.54.2.23410.1623/hysj.54.2.234Search in Google Scholar

Partal, T., Cigizoglu, H.K., Kahya, E., 2015. Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data. Stochastic Environmental Research and Risk Assessment, 29, 1317–1329. https://doi.org/10.1007/s00477-015-1061-110.1007/s00477-015-1061-1Search in Google Scholar

Percival, D.B., Walden, A.T., 2000. Wavelet methods for time series analysis. Cambridge Series in Statistical and Probabilistic Mathematics. 1st ed. Cambridge University Press, Cambridge.Search in Google Scholar

Ramana, R.V., Krishna, B., Kumar, S.R., Pandey, N.G., 2013. Monthly rainfall prediction using wavelet neural network analysis. Water Resources Management, 27, 3697–3711. https://doi.org/10.1007/s11269-013-0374-410.1007/s11269-013-0374-4Search in Google Scholar

Ramírez-Hernández, J., Infante-Prieto, S.O., Villa-Angulo, R., Hallack-Alegría, M., 2016. La influencia del efecto de borde en el pronóstico de precipitaciones utilizando DWT diádica, MODWT, ANN y ANFIS. Tecnología y ciencias del agua, 73, 93–113.Search in Google Scholar

Reichle, R.H., Liu, Q., Koster, R.D., Draper, C.S., Mahanama, S.P., Partyka, G.S., 2017. Land surface precipitation in MERRA-2. Journal of Climate, 30, 1643–1664. https://doi.org/10.1175/JCLI-D-16-0570.110.1175/JCLI-D-16-0570.1Search in Google Scholar

Rivera, D., Lillo, M., Uvo, C.B., Billib, M., Arumí, J.L., 2012. Forecasting monthly precipitation in Central Chile: a self-organizing map approach using filtered sea surface temperature. Theoretical and Applied Climatology, 107, 1–13. https://doi.org/10.1007/s00704-011-0453-510.1007/s00704-011-0453-5Search in Google Scholar

Sang, Y.F., 2012. A practical guide to discrete wavelet decomposition of hydrologic time series. Water Resources Management, 26, 3345–3365. https://doi.org/10.1007/s11269-012-0075-410.1007/s11269-012-0075-4Search in Google Scholar

Santos, C.A., Freire, P.K., Silva, R.M.D., Akrami, S.A., 2019. Hybrid wavelet neural network approach for daily inflow forecasting using Tropical Rainfall Measuring Mission data. Journal of Hydrologic Engineering, 24, Article Number: 04018062. https://doi.org/10.1061/(ASCE)HE.1943-5584.000172510.1061/(ASCE)HE.1943-5584.0001725Search in Google Scholar

Shoaib, M., Shamseldin, A.Y., Melville, B.W., Khan, M.M., 2016. A comparison between wavelet based static and dynamic neural network approaches for runoff prediction. Journal of Hydrology, 535, 211–225. http://dx.doi.org/10.1016/j.jhydrol.2016.01.07610.1016/j.jhydrol.2016.01.076Search in Google Scholar

Siad, S.M., Iacobellisb, V., Zdrulie, P., Gioiab, A., Stavid, I., Hoogenboom, G., 2019. A review of coupled hydrologic and crop growth models. Agricultural Water Management, 224, Article Number: 105746.10.1016/j.agwat.2019.105746Search in Google Scholar

Silva, I.D., Spatti, D.H., Flauzino, R.A., 2010. Redes neurais artificiais para engenharia e ciências aplicadas. Artliber, São Paulo, Brasil, 646 p.Search in Google Scholar

Sulaiman, S.O., Shiri, J., Shiralizadeh, H., Kisi, O., Yaseen, Z.M., 2018. Precipitation pattern modeling using cross-station perception: regional investigation. Environmental Earth Sciences, 77, 709. https://doi.org/10.1007/s12665-018-7898-010.1007/s12665-018-7898-0Search in Google Scholar

Tealab, A., Hefny, H., Badr, A., 2017. Forecasting of nonlinear time series using ANN. Future Computing and Informatics Journal, 2, 39–47. https://doi.org/10.1016/j.fcij.2017.05.00110.1016/j.fcij.2017.05.001Search in Google Scholar

Teodoro, P.E., de Oliveira-Júnior, J.F., Da Cunha, E.R., Correa, C.C.G., Torres, F.E., Bacani, V.M., Ribeiro, L.P., 2016. Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Mato Grosso do Sul State, Brazil. Meteorology and Atmospheric Physics, 128, 197–209. DOI: 10.1007/s00703-015-0408-y10.1007/s00703-015-0408-ySearch in Google Scholar

Wang, X.Y., Li, X., Zhu, J., Tanajura, C.A., 2018. The strengthening of Amazonian precipitation during the wet season driven by tropical sea surface temperature forcing. Environmental Research Letters, 13, Article Number: 094015. https://doi.org/10.1088/1748-9326/aadbb910.1088/1748-9326/aadbb9Search in Google Scholar

Wilks, D.S., 1989. Conditioning stochastic daily precipitation models on total monthly precipitation. Water Resources Research, 25, 1429–1439. https://doi.org/10.1029/WR025i006p0142910.1029/WR025i006p01429Search in Google Scholar

Wilks, D.S., 1999. Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Agricultural and Forest Meteorology, 93, 153–169. https://doi.org/10.1016/S0168-1923(98)00125-710.1016/S0168-1923(98)00125-7Search in Google Scholar

Yaseen, Z.M., Jaafar, O., Deo, R.C., Kisi, O., Adamowski, J., Quilty, J., El-Shafie, A., 2016. Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. Journal of Hydrology, 542, 603–614. http://dx.doi.org/10.1016/j.jhydrol.2016.09.03510.1016/j.jhydrol.2016.09.035Search in Google Scholar

Zhang, X., Peng, Y., Zhang, C., Wang, B., 2015. Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences. J. Hydrol., 530, 137–152. http://dx.doi.org/10.1016/j.jhydrol.2015.09.04710.1016/j.jhydrol.2015.09.047Search in Google Scholar

Zeri, M., Cunha-Zeri, G., Gois, G., Lyra, G.B., Oliveira-Júnior, J.F., 2019. Exposure assessment of rainfall to inter-annual variability using the wavelet transform. International Journal of Climatology, 39, 568–578. https://doi.org/10.1002/joc.581210.1002/joc.5812Search in Google Scholar