Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia

Atmospheric Research

Volumn 161-162, Issue , 2015, Pages 65-81

  Deo, Ravinesh C ^a   Şahin, Mehmet ^b  

^a UNIVERSITY OF SOUTHERN QUEENSLAND   (Australia)

^b SIIRT UNIVERSITY   (Turkey)

Author keywords

Artificial Neural Network; Data driven model; Drought prediction in Australia; Standardized Precipitation and Evapotranspiration Index (SPEI)

Indexed keywords

ATMOSPHERIC PRESSURE; ATMOSPHERIC TEMPERATURE; BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; CHEMICAL ACTIVATION; DROUGHT; EVAPOTRANSPIRATION; FORECASTING; FUNCTION EVALUATION; LEARNING ALGORITHMS; MEAN SQUARE ERROR; NEURAL NETWORKS; NEURONS; NONLINEAR PROGRAMMING; OCEANOGRAPHY; RAIN; SURFACE WATERS;

ARTIFICIAL NEURAL NETWORK MODELING; AUSTRALIA; BROYDEN-FLETCHER-GOLDFARB-SHANNO; COEFFICIENT OF DETERMINATION; DATA-DRIVEN MODEL; LEVENBERG-MARQUARDT LEARNING ALGORITHMS; SEA SURFACE TEMPERATURE (SST); STANDARDIZED PRECIPITATION AND EVAPOTRANSPIRATION INDEX (SPEI);

CLIMATE MODELS;

AIR TEMPERATURE; ARTIFICIAL NEURAL NETWORK; CLIMATE PREDICTION; DROUGHT; EVAPOTRANSPIRATION; HYDROMETEOROLOGY; PRECIPITATION ASSESSMENT; RAINFALL; SEA SURFACE TEMPERATURE;

AUSTRALIA;

EID: 84928136722     PISSN: 01698095     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.atmosres.2015.03.018     Document Type: Article

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