Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

[1]  C. Willmott.  Some Comments on the Evaluation of Model Performance , 1982 .

[2]  Kevin E. Trenberth,et al.  Signal Versus Noise in the Southern Oscillation , 1984 .

[3]  Neville Nicholls,et al.  A historical annual temperature dataset for Australia , 1996 .

[4]  J. Wallace,et al.  ENSO-like Interdecadal Variability: 1900–93 , 1997 .

[5]  J. Wallace,et al.  A Pacific Interdecadal Climate Oscillation with Impacts on Salmon Production , 1997 .

[6]  Neville Nicholls,et al.  An extended high-quality historical rainfall dataset for Australia , 1997 .

[7]  Shin'ichi Tamura,et al.  Capabilities of a four-layered feedforward neural network: four layers versus three , 1997, IEEE Trans. Neural Networks.

[8]  유효강수지수를 이용한 홍수위험의 정량적 진단 , 1998 .

[9]  Robert J. Kuligowski,et al.  Experiments in Short-Term Precipitation Forecasting Using Artificial Neural Networks , 1998 .

[10]  K. Hennessy,et al.  Trends in total rainfall, heavy rain events and number of dry days in Australia, 1910–1990 , 1998 .

[11]  D. Wilhite,et al.  Objective Quantification of Drought Severity and Duration , 1999 .

[12]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[13]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[14]  Ashish Sharma,et al.  A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting , 2000 .

[15]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[16]  N. Nicholls,et al.  TRENDS IN EXTREME RAINFALL INDICES FOR AN UPDATED HIGH QUALITY DATA SET FOR AUSTRALIA, 1910-1998 , 2000 .

[17]  Ashish Sharma,et al.  An application of artificial neural networks for rainfall forecasting , 2001 .

[18]  George E. Nasr,et al.  Neural Networks in Forecasting Electrical Energy Consumption , 2001, The Florida AI Research Society.

[19]  Arif Hepbasli,et al.  Comparison of solar radiation correlations for İzmir, Turkey , 2002 .

[20]  David I. Stern,et al.  Cointegration analysis of hemispheric temperature relations , 2002 .

[21]  Guang-Bin Huang,et al.  Learning capability and storage capacity of two-hidden-layer feedforward networks , 2003, IEEE Trans. Neural Networks.

[22]  Juan B. Valdés,et al.  NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .

[23]  G. Marshall.  Trends in the Southern Annular Mode from Observations and Reanalyses , 2003 .

[24]  Eric G. Blackman,et al.  Temperature response of Earth to the annual solar irradiance cycle , 2004 .

[25]  Adnan Sözen,et al.  Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle , 2004 .

[26]  N. Saji,et al.  Indian Ocean Dipole mode events and austral surface air temperature anomalies , 2005 .

[27]  Myles R. Allen,et al.  Attribution of global surface warming without dynamical models , 2005 .

[28]  J. V. Revadekar,et al.  Global observed changes in daily climate extremes of temperature and precipitation , 2006 .

[29]  S. Morid,et al.  Comparison of seven meteorological indices for drought monitoring in Iran , 2006 .

[30]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[31]  Surajit Chattopadhyay.  Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India , 2006, nlin/0609014.

[32]  S. Morid,et al.  Drought forecasting using artificial neural networks and time series of drought indices , 2007 .

[33]  D. Thompson,et al.  Australian Rainfall and Surface Temperature Variations Associated with the Southern Hemisphere Annular Mode , 2007 .

[34]  R. Deo,et al.  Modeling the impact of historical land cover change on Australia's regional climate , 2007 .

[35]  I. Smith,et al.  Characteristics of the Northern Australian Rainy Season , 2008 .

[37]  Mohsen Nasseri,et al.  Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network , 2008, Expert Syst. Appl..

[38]  Ranvir Singh,et al.  Study of indices for drought characterization in KBK districts in Orissa (India) , 2008 .

[39]  R. Deo,et al.  Impact of historical land cover change on daily indices of climate extremes including droughts in eastern Australia , 2009 .

[40]  Muhsin Tunay Gençoglu,et al.  Prediction of flashover voltage of insulators using least squares support vector machines , 2009, Expert Syst. Appl..

[41]  R. Deo,et al.  A continent under stress: interactions, feedbacks and risks associated with impact of modified land cover on Australia's climate , 2009 .

[42]  H. Byun,et al.  Evaluation, modification, and application of the Effective Drought Index to 200-Year drought climatology of Seoul, Korea , 2009 .

[43]  H. Byun,et al.  Future pattern of Asian drought under global warming scenario , 2009 .

[44]  D. Jones,et al.  High-quality spatial climate data-sets for Australia , 2009 .

[45]  H. Hendon,et al.  Representation and prediction of the Indian Ocean dipole in the POAMA seasonal forecast model , 2009 .

[46]  Kwok-Wing Chau,et al.  Data-driven models for monthly streamflow time series prediction , 2010, Eng. Appl. Artif. Intell..

[47]  Roger Stone,et al.  A comparison of two seasonal rainfall forecasting systems for Australia , 2010 .

[48]  Robin van den Honert,et al.  The 2011 Brisbane Floods: Causes, Impacts and Implications , 2011 .

[49]  H. Byun,et al.  A Spatiotemporal Analysis of Historical Droughts in Korea , 2011 .

[50]  Heikki Kauppi,et al.  Reconciling anthropogenic climate change with observed temperature 1998–2008 , 2011, Proceedings of the National Academy of Sciences.

[51]  Dominic A. Hudson,et al.  Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia , 2011 .

[52]  R. Deo.  Links between native forest and climate in Australia , 2011 .

[53]  Somia A. Asklany,et al.  Rainfall events prediction using rule-based fuzzy inference system , 2011 .

[54]  Sou-Sen Leu,et al.  Probabilistic prediction of tunnel geology using a Hybrid Neural-HMM , 2011, Eng. Appl. Artif. Intell..

[55]  M. Paulescu,et al.  A temperature‐based model for global solar irradiance and its application to estimate daily irradiation values , 2011 .

[56]  K. C. Tripathi,et al.  Prediction of Indian summer monsoon rainfall using Niño indices: A neural network approach , 2011 .

[57]  J. Adamowski,et al.  A wavelet neural network conjunction model for groundwater level forecasting , 2011 .

[58]  J. Abbot,et al.  Application of artificial neural networks to rainfall forecasting in Queensland, Australia , 2012, Advances in Atmospheric Sciences.

[59]  M. Şahin,et al.  Modelling of air temperature using remote sensing and artificial neural network in Turkey , 2012 .

[60]  Sancho Salcedo-Sanz,et al.  Accurate local very short-term temperature prediction based on synoptic situation Support Vector Regression banks , 2012 .

[61]  Anteneh Meshesha Belayneh,et al.  Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression , 2012, Appl. Comput. Intell. Soft Comput..

[62]  V. Singh,et al.  Comparison of multi-monthly rainfall-based drought severity indices, with application to semi-arid Konya closed basin, Turkey , 2012 .

[63]  Aurel F. Moise,et al.  Climate projections for Australia: a first glance at CMIP5 , 2013 .

[64]  M. Masinde.  Artificial neural networks models for predicting effective drought index: Factoring effects of rainfall variability , 2014, Mitigation and Adaptation Strategies for Global Change.

[65]  M. Imteaz,et al.  Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes , 2013 .

[66]  B. K. Panigrahi,et al.  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 , 2014, Climate Dynamics.

[67]  B. Timbal,et al.  The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society , 2013 .

[68]  Yılmaz Kaya,et al.  Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data , 2013 .

[69]  Pedro Antonio Gutiérrez,et al.  Simultaneous modelling of rainfall occurrence and amount using a hierarchical nominal-ordinal support vector classifier , 2014, Eng. Appl. Artif. Intell..

[70]  Mehmet Şahin,et al.  Application of extreme learning machine for estimating solar radiation from satellite data , 2014 .

[71]  Athanasios G. Paliatsos,et al.  Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece , 2014 .

[72]  J. Abbot,et al.  Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks , 2014 .

[73]  Sancho Salcedo-Sanz,et al.  Accurate precipitation prediction with support vector classifiers: A study including novel predictive variables and observational data , 2014 .