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Drought Modelling Based on Artificial Intelligence and Neural Network Algorithms: A Case Study in Queensland, Australia

Kavina Dayal, Ravinesh Deo and Armando A. Apan

List of Acronyms

ANN Artificial Neural Networks

AWAP Australian Water Availability Project

BFGS quasi-Newton Broyden-Fletcher-Goldfarb-Shanno quasi-Newton

d Willmott’s Index of Agreement

E Nash-Sutcliffe Coefficient of Efficiency

LM Levenberg-Marquardt training algorithm

Logsig Logarithmic sigmoid

MAE Mean Absolute Error

NRM Natural Resource Management

PE Prediction Error

PET Potential Evapotranspiration

Q1 First quartile (25th percentile)

Q2 Second quartile (50th percentile or median)

Q3 Third quartile (75th percentile)

R² Coefficient of Determination

RMSE Root Mean Squared Error

SPEI Standardized Precipitation-Evapotranspiration Index

Tansig Tangent sigmoid

Trainbfg Training BFGS quasi-Newton

Trainlm Training Levenberg-Marquardt

K. Dayal (&) • R. Deo

School of Agricultural Computational and Environmental Sciences,

International Centre of Applied Climate Sciences (ICACS),

University of Southern Queensland, Springfield, Australia e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

A.A. Apan

School of Civil Engineering and Surveying, University of Southern Queensland, Springfield, Australia

© Springer International Publishing AG 2017

W. Leal Filho (ed.), Climate Change Adaptation in Pacific Countries,

Climate Change Management, DOI 10.1007/978-3-319-50094-2_11

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