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Open AccessArticle

Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models

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Department of Hydrology and Water Resources, University of Venda, Thohoyandou 0950, South Africa
2
Department of Statistics, University of Venda, Thohoyandou 0950, South Africa
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Unit for Environmental Sciences and Management, North-West University, Vanderbijlpark 1900, South Africa
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(10), 4006; https://doi.org/10.3390/su12104006
Received: 8 April 2020 / Revised: 6 May 2020 / Accepted: 7 May 2020 / Published: 14 May 2020
Forecasting extreme hydrological events is critical for drought risk and efficient water resource management in semi-arid environments that are prone to natural hazards. This study aimed at forecasting drought conditions in a semi-arid region in north-eastern South Africa. The Standardized Precipitation Evaporation Index (SPEI) was used as a drought-quantifying parameter. Data for SPEI formulation for eight weather stations were obtained from South Africa Weather Services. Forecasting of the SPEI was achieved by using Generalized Additive Models (GAMs) at 1, 6, and 12 month timescales. Time series decomposition was done to reduce time series complexities, and variable selection was done using Lasso. Mild drought conditions were found to be more prevalent in the study area compared to other drought categories. Four models were developed to forecast drought in the Luvuvhu River Catchment (i.e., GAM, Ensemble Empirical Mode Decomposition (EEMD)-GAM, EEMD-Autoregressive Integrated Moving Average (ARIMA)-GAM, and Forecast Quantile Regression Averaging (fQRA)). At the first two timescales, fQRA forecasted the test data better than the other models, while GAMs were best at the 12 month timescale. Root Mean Square Error values of 0.0599, 0.2609, and 0.1809 were shown by fQRA and GAM at the 1, 6, and 12 month timescales, respectively. The study findings demonstrated the strength of GAMs in short- and medium-term drought forecasting. View Full-Text
Keywords: drought; forecasting; generalized additive models; hydrological extremes; SPEI; water resources; variable of importance drought; forecasting; generalized additive models; hydrological extremes; SPEI; water resources; variable of importance
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MDPI and ACS Style

Mathivha, F.; Sigauke, C.; Chikoore, H.; Odiyo, J. Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models. Sustainability 2020, 12, 4006. https://doi.org/10.3390/su12104006

AMA Style

Mathivha F, Sigauke C, Chikoore H, Odiyo J. Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models. Sustainability. 2020; 12(10):4006. https://doi.org/10.3390/su12104006

Chicago/Turabian Style

Mathivha, Fhumulani; Sigauke, Caston; Chikoore, Hector; Odiyo, John. 2020. "Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models" Sustainability 12, no. 10: 4006. https://doi.org/10.3390/su12104006

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