A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya's Operational Drought Monitoring  
[PERSON]  
1 School of Computing and Informatics, University of Nairobi (UoN), P.O. Box 30197, Nairobi 00100 GPO, Kenya; [EMAIL_ADDRESS] (R.O. ); [EMAIL_ADDRESS] (P.W.W. )1 School of Computing and Informatics, University of Nairobi (UoN), P.O. Box 30197, Nairobi 00100 GPO, Kenya; [EMAIL_ADDRESS] (P.W.W. )1 School of Computing and Informatics, University of Nairobi (UoN), P.O. Box 30197, Nairobi 00100 GPO, Kenya  
1 School of Computing and Informatics, University of Nairobi (UoN), P.O. Box 30197, Nairobi 00100 GPO, Kenya; [EMAIL_ADDRESS] (R.O. ); [EMAIL_ADDRESS] (P.W.W.) 1 School of Computing and Informatics, University of Nairobi (UoN), P.O. Box 30197, Nairobi 00100 GPO, Kenya
effects on surface to sub-surface water sources, followed by reduced vegetation growth and finally a culmination on socio-economic effects on people and livelihoods. There is an increase in both the frequency of droughs and the cost of economic losses as a result of droughs, particularly in the Greater Horn of Africa (GHA). For example, Government of Kenya [5] documents the 2008-2011 drought in Kenya as having made 3.7 million people food insecure, with economic losses approximated at 12.1 billion US dollars. The 2014 California drought was projected to have cost a total of 2.2 billion US dollars in losses [6]. Further documentation of losses due to drought, especially at household level, are reviewed in [PERSON] et al. [7] and in [PERSON] et al. [8]. The losses from droughs has necessitated a shift from reactive systems to drought risk management, which is characterized by both early warning systems and drought mitigation. As advocated by [PERSON] et al. [9], drought risk identification and drought early warning systems are the starting points to a sound drought risk management that can greatly reduce the severity of social and economic damage by droughs. On the other hand, drought risk mitigation aims to reduce impacts of droughs. Drought early warning systems (EWS) are in most cases based on remote sensing data, and in some cases socio-economic data is incorporated to measure the impacts of droughs [10]. Remote sensing data is documented to permit a cost-effective spatio-temporal assessment of vegetation conditions and crop yield [11] and the assessment of the performance of seasonal vegetation development like the case in [PERSON] et al. [12]. [PERSON] et al. [12] uses fraction of absorbed photosynthetically active radiation (FAPAR) to compute a seasonal proxy of gross primary production (GPP) in the context of the Horn of Africa. Remote sensed data on precipitation and vegetation are some of the most used in drought monitoring. Important precipitation indicators include Rainfall Estimates (RFE) and the rainfall anomaly-based indices like Rainfall Condition Index (RCI) as calculated in the form of Precipitation Condition Index (PCI) in [PERSON] et al. [13] and Standardized Precipitation Index (SPI) [14]. Other precipitation derived composite indices includes Crop Moisture Index (CMI) [15], and Standardized Precipitation-Evapotranspiration Index (SPEI) [16]. Vegetation-based indices includes the Normalized Difference Vegetation Index (NDVI) and its anomalies like the Vegetation Condition Index (VCI). A comprehensive review of the drought indices is found in [PERSON] and [PERSON] [17]. The use of SPI in drought monitoring and also in drought forecasting is well documented in [PERSON] et al [2], in [PERSON] et al. [18], [PERSON] [19] and [PERSON] [20]. The VCI is widely used, for example in the US Drought Monitor (USDM) [21]. In the USDM, the VCI is used as a component of the vegetation health index (VHI) that is one of the six key physical indicators used at national scale. [PERSON] and [PERSON] [22] documents the drought monitoring system for Kenya as implemented by the University of Natural Resources and Life Sciences, Vienna (BOKU).