Drought modelling based on artificial intelligence and neural network algorithms: a case study in Queensland, Australia

Dayal, Kavina and Deo, Ravinesh and Apan, Armando A. (2017) Drought modelling based on artificial intelligence and neural network algorithms: a case study in Queensland, Australia. In: Climate change adaptation in Pacific countries: fostering resilience and improving the quality of life (Part II). Climate Change Management. Springer International Publishing, pp. 177-198. ISBN 978-3-319-50093-5

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.


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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 09 Jun 2017 06:57
Last Modified: 19 Sep 2018 01:28
Uncontrolled Keywords: Artificial Neural Network; climate modelling; data-driven model; drought prediction in Australia; Standardized Precipitation-Evapotranspiration Index
Fields of Research : 04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
04 Earth Sciences > 0401 Atmospheric Sciences > 040105 Climatology (excl.Climate Change Processes)
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
Socio-Economic Objective: D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960202 Atmospheric Processes and Dynamics
Identification Number or DOI: 10.1007/978-3-319-50094-2_11
URI: http://eprints.usq.edu.au/id/eprint/30867

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