Application of artificial neural networks in forecasting a standardized precipitation evapotranspiration index for the upper blue nile basin
Application of artificial neural networks in forecasting a standardized precipitation evapotranspiration index for the upper blue nile basin
The occurrence frequency of drought has intensified with the unprecedented effect of global warming. Knowledge about the spatiotemporal distributions of droughts and their trends is crucial for risk management and developing mitigation strategies. In this study, we developed seven artificial neural network (ANN) predictive models incorporating hydro-meteorological, climate, sea surface temperatures, and topographic attributes to forecast the standardized precipitation evapotranspiration index (SPEI) for seven stations in the Upper Blue Nile basin (UBN) of Ethiopia from 1986 to 2015. The main aim was to analyze the sensitivity of drought-trigger input parameters and to measure their predictive ability by comparing the predicted values with the observed values. Statistical comparisons of the different models showed that accurate results in predicting SPEI values could be achieved by including large-scale climate indices. Furthermore, it was found that the coefficient of determination and the root-mean-square error of the best architecture ranged from 0.820 to 0.949 and 0.263 to 0.428, respectively. In terms of statistical achievement, we concluded that ANNs offer an alternative framework for forecasting the SPEI drought index.
Mulualem, Getachew Mehabie
88668cda-20ef-4543-a7e7-0a5f0c993d7b
Liou, Yuei-An
09376ac5-cbac-493d-bda2-f55a0832c1f0
27 February 2020
Mulualem, Getachew Mehabie
88668cda-20ef-4543-a7e7-0a5f0c993d7b
Liou, Yuei-An
09376ac5-cbac-493d-bda2-f55a0832c1f0
Mulualem, Getachew Mehabie and Liou, Yuei-An
(2020)
Application of artificial neural networks in forecasting a standardized precipitation evapotranspiration index for the upper blue nile basin.
Water.
(doi:10.3390/w12030643).
Abstract
The occurrence frequency of drought has intensified with the unprecedented effect of global warming. Knowledge about the spatiotemporal distributions of droughts and their trends is crucial for risk management and developing mitigation strategies. In this study, we developed seven artificial neural network (ANN) predictive models incorporating hydro-meteorological, climate, sea surface temperatures, and topographic attributes to forecast the standardized precipitation evapotranspiration index (SPEI) for seven stations in the Upper Blue Nile basin (UBN) of Ethiopia from 1986 to 2015. The main aim was to analyze the sensitivity of drought-trigger input parameters and to measure their predictive ability by comparing the predicted values with the observed values. Statistical comparisons of the different models showed that accurate results in predicting SPEI values could be achieved by including large-scale climate indices. Furthermore, it was found that the coefficient of determination and the root-mean-square error of the best architecture ranged from 0.820 to 0.949 and 0.263 to 0.428, respectively. In terms of statistical achievement, we concluded that ANNs offer an alternative framework for forecasting the SPEI drought index.
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Accepted/In Press date: 25 February 2020
Published date: 27 February 2020
Identifiers
Local EPrints ID: 502930
URI: http://eprints.soton.ac.uk/id/eprint/502930
ISSN: 2073-4441
PURE UUID: 3f815bb4-49b3-42d1-8dcf-30e3c302967e
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Date deposited: 14 Jul 2025 16:38
Last modified: 22 Aug 2025 02:45
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Author:
Getachew Mehabie Mulualem
Author:
Yuei-An Liou
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