  Drought is a complex environmental phenomenon that affects millions of people
and communities all over the globe and is too elusive to be accurately
predicted. This is mostly due to the scalability and variability of the web of
environmental parameters that directly/indirectly causes the onset of different
categories of drought. Since the dawn of man, efforts have been made to
uniquely understand the natural indicators that provide signs of likely
environmental events. These indicators/signs in the form of indigenous
knowledge system have been used for generations. The intricate complexity of
drought has, however, always been a major stumbling block for accurate drought
prediction and forecasting systems. Recently, scientists in the field of
agriculture and environmental monitoring have been discussing the integration
of indigenous knowledge and scientific knowledge for a more accurate
environmental forecasting system in order to incorporate diverse environmental
information for a reliable drought forecast. Hence, in this research, the core
objective is the development of a semantics-based data integration middleware
that encompasses and integrates heterogeneous data models of local indigenous
knowledge and sensor data towards an accurate drought forecasting system for
the study areas. The local indigenous knowledge on drought gathered from the
domain experts is transformed into rules to be used for performing deductive
inference in conjunction with sensors data for determining the onset of drought
through an automated inference generation module of the middleware. The
semantic middleware incorporates, inter alia, a distributed architecture that
consists of a streaming data processing engine based on Apache Kafka for
real-time stream processing; a rule-based reasoning module; an ontology module
for semantic representation of the knowledge bases.
