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Artificial neural networks models for predicting effective drought index: Factoring effects of rainfall variability

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Abstract

Though most factors that trigger droughts cannot be prevented, accurate, relevant and timely forecasts can be used to mitigate their impacts. Drought forecasts must define the droughts severity, onset, cessation, duration and spatial distribution. Given the high probability of droughts occurrence in Kenya, her heavy reliance on rain-fed agriculture and lack of effective drought mitigation strategies, the country is highly vulnerable to impacts of droughts. Current drought forecasting approaches used in Kenya are not able to provide short and long term forecasts and they fall short of providing the severity of the drought. In this paper, a combination of Artificial Neural Networks and Effective Drought Index is presented as a potential candidate for addressing these drawbacks. This is demonstrated using forecasting models that were built using weather data for thirty years for four weather stations (representing 3 agro-ecological zones) in Kenya. Experiments varying various input/output combinations were carried out and drought forecasting network models were implemented in Matrix Laboratory’s (MATLAB) Neural Network Toolbox. The models incorporate forecasted rainfall values in order to mitigate for unexpected extreme climate variations. With accuracies as high as 98 %, the solution is a great enhancement to the solutions currently in use in Kenya.

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Acknowledgement

Special thanks to the Kenya Meteorological Department for providing the data set used in this research.

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Authors and Affiliations

  1. Department of Information Technology, Central University of Technology, Private Bag X20539, Bloemfontein, 9300, South Africa

    Muthoni Masinde

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  1. Muthoni Masinde
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Correspondence to Muthoni Masinde.

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Masinde, M. Artificial neural networks models for predicting effective drought index: Factoring effects of rainfall variability. Mitig Adapt Strateg Glob Change 19, 1139–1162 (2014). https://doi.org/10.1007/s11027-013-9464-0

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  • DOI: https://doi.org/10.1007/s11027-013-9464-0

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