Abstract
Droughts, with their increasing frequency of occurrence, especially in the Greater Horn of Africa (GHA), continue to negatively affect lives and livelihoods. For example, the 2011 drought in East Africa caused massive losses, documented to have cost the Kenyan economy over 12 billion US dollars. Consequently, the demand is ever-increasing for ex-ante drought early warning systems with the ability to offer drought forecasts with sufficient lead times The study uses 10 precipitation and vegetation condition indices that are lagged over 1, 2 and 3-month time-steps to predict future values of vegetation condition index aggregated over a 3-month time period (VCI3M) that is a proxy variable for drought monitoring. The study used data covering the period 2001–2015 at a monthly frequency for four arid northern Kenya counties for model training, with data for 2016–2017 used as out-of-sample data for model testing. The study adopted a model space search approach to obtain the most predictive artificial neural network (ANN) model as opposed to the traditional greedy search approach that is based on optimal variable selection at each model building step. The initial large model-space was reduced using the general additive model (GAM) technique together with a set of assumptions. Even though we built a total of 102 GAM models, only 20 had R2 ≥ 0.7, and together with the model with lag of the predicted variable, were subjected to the ANN modelling process. The ANN process itself uses the brute-force approach that automatically partitions the training data into 10 sub-samples, builds the ANN models in these samples and evaluates their performance using multiple metrics. The results show the superiority of 1-month lag of the variables as compared to longer time lags of 2 and 3 months. The best ANN model recorded an R2 of 0.78 between actual and predicted vegetation conditions 1-month ahead using the out-of-sample data. Investigated as a classifier distinguishing five vegetation deficit classes, the best ANN model had a modest accuracy of 67% and a multi-class area under the receiver operating characteristic curve (AUROC) of 89.99%.
Highlights
A drought is a recurrent event marked by lack of precipitation for extended period of times [1,2]
In Kenya, an operational drought risk management (DRM) system is in place, as droughts in the past led to food insecurity and heavy economic losses
Moderate resolution imaging spectroradiometer (MODIS) at 250 m ground resolution is the source of the vegetation data, while Tropical Applications of Meteorology using SATellite (TAMSAT)’s version 3 product [38] is the source of the precipitation data
Summary
A drought is a recurrent event marked by lack of precipitation for extended period of times [1,2]. The use of multiple indices and variables in Tadesse et al [34] stands out in the use of eleven variables from oceanic, environmental, climate and satellite data in the prediction of vegetation outlook (VegOut) This approach of using multiple indices in the prediction of vegetation conditions as a proxy of drought effects is documented in Tadesse et al [35] and Wardlow et al [36]. We used a multi-variate analysis approach which uses a combination of two techniques, one statistical and the other machine learning, to predict vegetation conditions and thereby predict drought conditions up to three months ahead. This approach evaluates and selects the model from the space of all possible models based on objective evaluation metrics. In Kenya, an operational drought risk management (DRM) system is in place, as droughts in the past led to food insecurity and heavy economic losses
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