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

Part of the Climate Change Management book series (CCM)

Abstract

The search for better climate change adaptation techniques for addressing environmental and economic issues due to changing climate is of paramount interest in the current era. One of the many ways Pacific Island regions and its people get affected is by dry spells and drought events from extreme climates. A drought is simply a prolonged shortage of water supply in an area. The impact of drought varies both temporally and spatially that can be catastrophic for such regions with lack of resources and facilities to mitigate the drought impacts. Therefore, forecasting drought events using predictive models that have practical implications for understanding drought hydrology and water resources management can allow enough time to take appropriate adaption measures. This study investigates the feasibility of the Artificial Neural Network (ANN) algorithms for prediction of a drought index: Standardized Precipitation-Evapotranspiration Index (SPEI). The purpose of the study was to develop an ANN model to predict the index in two selected regions in Queensland, Australia. The first region, is named as the grassland and the second as the temperate region. The monthly gridded meteorological variables (precipitation, maximum and minimum temperature) that acted as input parameters in ANN model were obtained from Australian Water Availability Project (AWAP) for 1915–2013 period. The potential evapotranspiration (PET), calculated using thornthwaite method, was also an input variable, while SPEI was the predictand for the ANN model. The input data were divided into training (80%), validation (10%) and testing (10%) sets. To determine the optimum ANN model, the Levenberg-Marquardt and Broyden-Fletcher-Goldfarb-Shanno quasi-Newton backpropagation algorithms were used for training the ANN network and the tangent sigmoid, logarithmic sigmoid and linear activation algorithms were used for hidden transfer and output functions. The best architecture of input-hidden neuron-output neurons was 4-28-1 and 4-27-1 for grassland and temperate region, respectively. For evaluation and selection of the optimum ANN model, the statistical metrics: Coefficient of Determination (R 2 ), Willmott’s Index of Agreement (d), Nash-Sutcliffe Coefficient of Efficiency (E), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were employed. The R 2 , d, E, RMSE and MAE for optimum ANN models were 0.9839, 0.9909, 0.9838, 0.1338, 0.0882 and 0.9886, 0.9935, 0.9874, 0.1198, 0.0814 for grassland and temperate region, respectively. When prediction errors were analysed, a value of 0.0025 to 0.8224 was obtained for the grassland region, and a value of 0.0113 to 0.6667 was obtained for the temperate region, indicating that the ANN model exhibit a good skill in predicting the monthly SPEI. Based on the evaluation and statistical analysis of the predicted SPEI and its errors in the test period, we conclude that the ANN model can be used as a useful data-driven tool for forecasting drought events. Broadly, the ANN model can be applied for prediction of other climate related variables, and therefore can play a vital role in the development of climate change adaptation and mitigation plans in developed and developing nations, and most importantly, in the Pacific Island Nations where drought events have a detrimental impact on economic development.

Keywords

  • Artificial neural network
  • Climate modelling
  • Data-driven model
  • Drought prediction in Australia
  • Standardized Precipitation-Evapotranspiration Index

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Abbreviations

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)

R 2 :

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

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Acknowledgements

The data were acquired from the Australian Water Availability Project (AWAP). Kavina Dayal was supported by the University of Southern Queensland Postgraduate Research Scholarship (USQ-PRS) and School of Computational, Agricultural and Environmental Sciences. We thank the reviewers and editor for their comments and feedback.

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Dayal, K., Deo, R., Apan, A.A. (2017). Drought Modelling Based on Artificial Intelligence and Neural Network Algorithms: A Case Study in Queensland, Australia. In: Leal Filho, W. (eds) Climate Change Adaptation in Pacific Countries. Climate Change Management. Springer, Cham. https://doi.org/10.1007/978-3-319-50094-2_11

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