Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia
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Abbot, 2012, Application of artificial neural networks to rainfall forecasting in Queensland, Australia, Adv. Atmos. Sci., 29, 717, 10.1007/s00376-012-1259-9
Abbot, 2014, Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks, Atmos. Res., 138, 166, 10.1016/j.atmosres.2013.11.002
Acharya, 2013, Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine, Clim. Dyn., 1–8
Adamowski, 2011, A wavelet neural network conjunction model for groundwater level forecasting, J. Hydrol., 407, 28, 10.1016/j.jhydrol.2011.06.013
Alexander, 2006, Global observed changes in daily climate extremes of temperature and precipitation, J. Geophys. Res. Atmos. (1984–2012), 111
Asklany, 2011, Rainfall events prediction using rule-based fuzzy inference system, Atmos. Res., 101, 228, 10.1016/j.atmosres.2011.02.015
Belayneh, 2012, Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression, Appl. Comput. Intell. Soft Comput., 2012, 6, 10.1155/2012/794061
Byun, 1998, Quantified diagnosis of flood possibility by using effective precipitation index, J. Korean Water Res. Assoc., 31, 657
Byun, 1999, Objective quantification of drought severity and duration, J. Clim., 12, 2747, 10.1175/1520-0442(1999)012<2747:OQODSA>2.0.CO;2
Byun, 2008, Study on the periodicities of droughts in Korea, Asia-Pac. J. Atmos. Sci., 44, 417
Chattopadhyay, 2007, Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India, Acta Geophys., 55, 369, 10.2478/s11600-007-0020-8
Day, 2010, Seasonal Pacific Ocean Temperature Analysis-1 (SPOTA-1) as at November 1, 2010
Deo, 2009, Impact of historical land cover change on daily indices of climate extremes including droughts in eastern Australia, Geophys. Res. Lett., 36, 10.1029/2009GL037666
Dijk, 2013, The Millennium Drought in southeast Australia (2001–2009): natural and human causes and implications for water resources, ecosystems, economy, and society, Water Resour. Res., 49, 1040, 10.1002/wrcr.20123
Dogan, 2012, Comparison of multi-monthly rainfall-based drought severity indices, with application to semi-arid Konya closed basin, Turkey, J. Hydrol., 470, 255, 10.1016/j.jhydrol.2012.09.003
Douglass, 2004, Temperature response of Earth to the annual solar irradiance cycle, Phys. Lett. A, 323, 315, 10.1016/j.physleta.2004.01.066
Fawcett, 2010, A comparison of two seasonal rainfall forecasting systems for Australia, Aust. Meteorol. Oceanogr. J., 60, 15, 10.22499/2.6001.002
Gencoglu, 2009, Prediction of flashover voltage of insulators using least squares support vector machines, Expert Syst. Appl., 36, 10789, 10.1016/j.eswa.2009.02.021
Govindaraju, 2000, Artificial neural networks in hydrology. II: hydrologic applications, J. Hydrol. Eng., 5, 124, 10.1061/(ASCE)1084-0699(2000)5:2(124)
Haylock, 2000, Trends in extreme rainfall indices for an updated high quality data set for Australia, 1910–1998, Int. J. Climatol., 20, 1533, 10.1002/1097-0088(20001115)20:13<1533::AID-JOC586>3.0.CO;2-J
Hendon, 2007, Australian rainfall and surface temperature variations associated with the Southern Hemisphere annular mode, J. Clim., 20, 2452, 10.1175/JCLI4134.1
Huang, 2003, Learning capability and storage capacity of two-hidden-layer feedforward networks, IEEE Trans. Neural. Netw., 14, 274, 10.1109/TNN.2003.809401
Huang, 2006, Extreme learning machine: theory and applications, Neurocomputing, 70, 489, 10.1016/j.neucom.2005.12.126
Hudson, 2011, Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia, Q. J. R. Meteorol. Soc., 137, 673, 10.1002/qj.769
Hurst
Inquiry, Q.F.C.o, 2011
Irving, 2012, Climate projections for Australia: a first glance at CMIP5, Aust. Meteorol. Oceanogr. J., 62, 211, 10.22499/2.6204.003
Jones, 2009, High-quality spatial climate data-sets for Australia, Aust. Meteorol. Oceanogr. J., 58, 233, 10.22499/2.5804.003
Kaufmann, 2002, Cointegration analysis of hemispheric temperature relations, J. Geophys. Res. Atmos. (1984–2012), 107, 8
Kaufmann, 2011, Reconciling anthropogenic climate change with observed temperature 1998–2008, Proc. Natl. Acad. Sci., 108, 11790, 10.1073/pnas.1102467108
Kim, 2009, Future pattern of Asian drought under global warming scenario, Theor. Appl. Climatol., 98, 137, 10.1007/s00704-008-0100-y
Kim, 2003, Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks, J. Hydrol. Eng., 8, 319, 10.1061/(ASCE)1084-0699(2003)8:6(319)
Kim, 2009, Evaluation, modification, and application of the Effective Drought Index to 200-Year drought climatology of Seoul, Korea, J. Hydrol., 378, 1, 10.1016/j.jhydrol.2009.08.021
Kim, 2011, A spatiotemporal analysis of historical droughts in Korea, J. Appl. Meteorol. Climatol., 50, 1895, 10.1175/2011JAMC2664.1
Kuligowski, 1998, Experiments in short-term precipitation forecasting using artificial neural networks, Mon. Weather Rev., 126, 470, 10.1175/1520-0493(1998)126<0470:EISTPF>2.0.CO;2
Lavery, 1997, An extended high-quality historical rainfall dataset for Australia, Aust. Meteorol. Mag., 46, 27
Leu, 2011, Probabilistic prediction of tunnel geology using a Hybrid Neural-HMM, Eng. Appl. Artif. Intell., 24, 658, 10.1016/j.engappai.2011.02.010
Luk, 2000, A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting, J. Hydrol., 227, 56, 10.1016/S0022-1694(99)00165-1
Maier, 2000, Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environ. Model Softw., 15, 101, 10.1016/S1364-8152(99)00007-9
Mantua, 1997, A Pacific interdecadal climate oscillation with impacts on salmon production, Bull. Am. Meteorol. Soc., 78, 1069, 10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2
Marshall, 2003, Trends in the Southern Annular Mode from observations and reanalyses, J. Clim., 16, 4134, 10.1175/1520-0442(2003)016<4134:TITSAM>2.0.CO;2
Masinde, 2013, Artificial neural networks models for predicting effective drought index: factoring effects of rainfall variability, Mitig. Adapt. Strateg. Glob. Chang., 1–24
McAlpine, 2007, Modeling the impact of historical land cover change on Australia's regional climate, Geophys. Res. Lett., 34, 10.1029/2007GL031524
McAlpine, 2009, A continent under stress: interactions, feedbacks and risks associated with impact of modified land cover on Australia's climate, Glob. Chang. Biol., 15, 2206, 10.1111/j.1365-2486.2009.01939.x
Mekanik, 2013, Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes, J. Hydrol., 503, 11, 10.1016/j.jhydrol.2013.08.035
Morid, 2006, Comparison of seven meteorological indices for drought monitoring in Iran, Int. J. Climatol., 26, 971, 10.1002/joc.1264
Morid, 2007, Drought forecasting using artificial neural networks and time series of drought indices, Int. J. Climatol., 27, 2103, 10.1002/joc.1498
Nasr, 2002, Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches, Int. J. Energy Res., 26, 67, 10.1002/er.766
Nasseri, 2008, Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network, Expert Syst. Appl., 35, 1415, 10.1016/j.eswa.2007.08.033
Nastos, 2014, Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece, Atmos. Res., 144, 141, 10.1016/j.atmosres.2013.11.013
Ortiz-García, 2012, Accurate local very short-term temperature prediction based on synoptic situation Support Vector Regression banks, Atmos. Res., 107, 1, 10.1016/j.atmosres.2011.10.013
Ortiz-García, 2014, Accurate precipitation prediction with support vector classifiers: a study including novel predictive variables and observational data, Atmos. Res., 139, 128, 10.1016/j.atmosres.2014.01.012
Pandey, 2008, Study of indices for drought characterization in KBK districts in Orissa (India), Hydrol. Process., 22, 1895, 10.1002/hyp.6774
Paulescu, 2011, A temperature‐based model for global solar irradiance and its application to estimate daily irradiation values, Int. J. Energy Res., 35, 520, 10.1002/er.1709
Rajesh, 2011, Extreme learning machines—a review and state-of-the-art, Int. J. Wisdom Based Comput., 1, 35
Şahin, 2012, Modelling of air temperature using remote sensing and artificial neural network in Turkey, Adv. Space Res., 50, 973, 10.1016/j.asr.2012.06.021
Şahin, 2013, Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data, Adv. Space Res., 51, 891, 10.1016/j.asr.2012.10.010
Şahin, 2014, Application of extreme learning machine for estimating solar radiation from satellite data, Int. J. Energy Res., 38, 205, 10.1002/er.3030
Saji, 2005, Indian Ocean Dipole mode events and austral surface air temperature anomalies, Dyn. Atmos. Oceans, 39, 87, 10.1016/j.dynatmoce.2004.10.015
Sánchez-Monedero, 2014, Simultaneous modelling of rainfall occurrence and amount using a hierarchical nominal–ordinal support vector classifier, Eng. Appl. Artif. Intell., 34, 199, 10.1016/j.engappai.2014.05.016
Seqwater, 2011
Shukla, 2011, Prediction of Indian summer monsoon rainfall using Niño indices: a neural network approach, Atmos. Res., 102, 99, 10.1016/j.atmosres.2011.06.013
Smith, 2008, Characteristics of the northern Australian rainy season, J. Clim., 21, 4298, 10.1175/2008JCLI2109.1
Sözen, 2004, Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle, Appl. Energy, 79, 309, 10.1016/j.apenergy.2003.12.012
Stone, 2005, Attribution of global surface warming without dynamical models, Geophys. Res. Lett., 32, 10.1029/2005GL023682
Suppiah, 1998, Trends in total rainfall, heavy rain events and number of dry days in Australia, 1910–1990, Int. J. Climatol., 18, 1141, 10.1002/(SICI)1097-0088(199808)18:10<1141::AID-JOC286>3.0.CO;2-P
Tamura, 1997, Capabilities of a four-layered feedforward neural network: four layers versus three, IEEE Trans. Neural. Netw., 8, 251, 10.1109/72.557662
Torok, 1996, A historical annual temperature dataset, Aust. Meteorol. Mag., 45
Trenberth, 1984, Signal versus noise in the Southern Oscillation, Mon. Weather Rev., 112, 326, 10.1175/1520-0493(1984)112<0326:SVNITS>2.0.CO;2
Ulgen, 2002, Comparison of solar radiation correlations for Izmir, Turkey, Int. J. Energy Res., 26, 413, 10.1002/er.794
van den Honert, 2011, The 2011 Brisbane floods: causes, impacts and implications, Water, 3, 1149, 10.3390/w3041149
Willmott, 1982, Some comments on the evaluation of model performance, Bull. Am. Meteorol. Soc., 63, 1309, 10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2
Wu, 2010, Data-driven models for monthly streamflow time series prediction, Eng. Appl. Artif. Intell., 23, 1350, 10.1016/j.engappai.2010.04.003
Zhang, 1997, ENSO-like interdecadal variability: 1900–93, J. Clim., 10, 1004, 10.1175/1520-0442(1997)010<1004:ELIV>2.0.CO;2