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A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring

1
School of Computing and Informatics, University of Nairobi (UoN), P.O. Box 30197, Nairobi 00100 GPO, Kenya
2
National Drought Management Authority (NDMA), Lonrho House -Standard Street, Box 53547, Nairobi 00200, Kenya
3
Institute of Surveying, Remote Sensing and Land Information, University of Natural Resources (BOKU), Peter Jordan Strasse 82, A-1190 Vienna, Austria
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1099; https://doi.org/10.3390/rs11091099
Received: 23 January 2019 / Revised: 11 April 2019 / Accepted: 2 May 2019 / Published: 8 May 2019
(This article belongs to the Special Issue Remote Sensing of Drought Monitoring)
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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%. View Full-Text
Keywords: general additive model; drought risk management; early warning system; model selection; overfitting; cross-validation general additive model; drought risk management; early warning system; model selection; overfitting; cross-validation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Adede, C.; Oboko, R.; Wagacha, P.W.; Atzberger, C. A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring. Remote Sens. 2019, 11, 1099.

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