A Framework for Accurate Drought Forecasting
System Using Semantics-Based Data Integration
Middleware

arXiv:1706.07294v1 [cs.DB] 20 Jun 2017

Adeyinka K. Akanbi and Muthoni Masinde
Department of Information Technology
Central University of Technology, Free State, South Africa
{aakanbi,emasinde}@cut.ac.za

Abstract. Technological advancement in Wireless Sensor Networks (WSN)
has made it become an invaluable component of a reliable environmental monitoring system; they form the ’digital skin’ through which to
’sense’ and collect the context of the surroundings and provides information on the process leading to complex events such as drought. However,
these environmental properties are measured by various heterogeneous
sensors of different modalities in distributed locations making up the
WSN, using different abstruse terms and vocabulary in most cases to
denote the same observed property, causing data heterogeneity. Adding
semantics and understanding the relationships that exist between the
observed properties, and augmenting it with local indigenous knowledge
is necessary for an accurate drought forecasting system. In this paper, we
propose the framework for the semantic representation of sensor data and
integration with indigenous knowledge on drought using a middleware
for an efficient drought forecasting system.
Key words: middleware, internet of things, drought forecasting, semantic integration,ontology, interoperability, semantic technology

1 Introduction
The application of Semantic Technology for drought forecasting is a growing research area. Our work investigates the semantic representation and integration of
measured environmental entities with the local Indigenous knowledge (IK) using
an ontology to allow reasoning and generate inference based on their interrelationship. We present a proposed model which outline our research directions,
[7] provides a further overview of the framework towards an accurate drought
forecasting system.
In terms of negative impacts, droughts are currently ranked 1 number one
(CRED 2012). Compared to other natural disasters such as floods, hurricanes,
earthquakes and epidemics, droughts are very difficult to predict; they creep
1

The ranking is based on severity, length of event, total area affected, total loss of life,
total economic loss, social effect, long-term impacts, suddenness and frequency[1].

2

A.K. Akanbi & Muthoni Masinde

slowly and last longest. The complex nature of droughts onset-termination has
made it acquire the title ”the creeping disaster” [2]. The greatest challenge is
designing a framework which can track information about the ’what’, ’where’
and ’when’ of environmental phenomena and the representation of the various
dynamic aspects of the phenomena [3]. The representation of such phenomena
requires better understanding of the ’process’ that leads to the ’event’. For example, a soil moisture sensor provides sets of values for the observed property
soil moisture. The measured property can also be influenced by the temperature heat index measured over the observed period. This makes accurate prediction based on these sensor values almost impossible without understanding
the semantics and relationships that exist between this various properties. Hypothetically, drought prediction tools could be used to establish precise drought
development patterns as early as possible and provide sufficient information to
decision-makers to prepare for the droughts long before they happen. This way,
the prediction can be used to mitigate effects of droughts.
The technological advancement in Wireless Sensor Networks (WSN) has facilitated its use in monitoring environmental properties irrespective of the geographical location. In their (WSNs) current implementation, these properties
are measured using heterogeneous sensors that are mostly distributed in different locations. Further, different abstruse terms and vocabulary in most cases
are used to denote the same observed property, thereby leading to data heterogeneity. Moreover, research [4], [5] on indigenous knowledge (IK) on droughts
has pointed to the fact that IK on living and non-living things e.g., sifennefene
worms, peulwane birds, lehota frogs and plants like mutiga tree, mothokolo tree
etc can indicate drier or wetter conditions, which can imply likely occurrence of
drought event over time [6]. This scenario shows that environmental events can
be inferred from sensors data augmented with IK, if proper semantic is attached
to it based on some set of indicators. Therefore, a semantics-based data integration middleware is required to bridge the gap between heterogeneous sensor
data and IK for an accurate drought forecasting and prediction system.

2 Problem Statements
The following problems were identified as a major bottleneck for the utilization
of semantic technologies for drought forecasting:
The current lack of ontology based middleware for the semantic representation of
environmental process: Ontological modeling of key concepts of environmental
phenomena such as object, state, process and event, ensures the drawing of accurate inference from the sequence of processes that lead to an event. Presently,
what is currently missing is an environmental ontology with well-defined vocabularies that allow explicit representation of the process, events and also attach
semantics to the participants in the environmental domain.
Lack of semantic integration of heterogeneous data sources with indigenous
knowledge for an accurate environmental forecasting: Studies reveal that over
80% of farmers in some parts of Kenya, Zambia, Zimbabwe and South Africa

Semantics-Based Data Integration Middleware

3

rely on Indigenous knowledge forecasts (IKF) for their agricultural practices [5].
An IoT-based environmental monitoring system made up of interconnected heterogeneous weather information sources such as sensors, mobile phones, conventional weather stations, and indigenous knowledge could improve the accuracy
of environmental forecasting.
Lack of IoT-based drought forecasts communication and dissemination channels:
There is a lack of effective dissemination channels for drought forecasting information. For example, the absence of smart billboards placed at strategic location and smart phones. The output channels would ensure farmers have access
to drought forecasting information know the spatial distribution of a drought
vulnerability index.

3 Research Questions
To what extent does the adoption of knowledge representation and semantic technology in the development of a middleware enable seamless sharing and exchange
of data among heterogeneous IoT entities?
Several standards have been created to cope with the data heterogeneities. Examples are the Sensor Markup Language (SensorML) 2 , WaterML, and American Federal Geographic Data (FGDC) Standard 3 . However, these standards
provide sensor data to a predefined application in a standardized format, and
hence do not generally solve data heterogeneity. Semantic technology solves this
by representing data in a machine readable language such as Resource Description Framework (RDF) and Ontology Web Language (OWL), for seamless data
sharing irrespective of the domain.
What are the main components of an implementation framework/architecture
that employs the middleware to implement an IoT-based Drought Early Warning Systems (DEWS)?
The existence of ontology with well-defined vocabularies that allows an explicit
representation of process and events; the representation and integration of the
inputs in machine-readable formats, the availability of a reasoning engine (CEP
Engine) that generates inference based on input parameters.

4 Methodology
The proposed semantic middleware is a software layer composed of a set of
various sub-layers interposed between the application layer and the physical
layer. It incorporates interface protocols, which liaise with the storage database
in the cloud for downloading the semi-processed sensory reading to be represented based on the ontology through a mediator device as shown in figure 3[7].
An environmental process-based ontology is required to overcome the problems
2
3

http://www.opengeospatial.org/standards
https://www.fgdc.gov/metadata

4

A.K. Akanbi & Muthoni Masinde

associated with the dynamic nature of environmental data and the data heterogeneities. The study proposes to use DOLCE top-level ontology for the modelling of the foundational entities needed to represent the dynamic phenomena.
Information from the sensor data streams is integrated with indigenous knowledge using a Complex Events Processing (CEP ) engine as proposed in figure 1.
This will serve as the reasoning engine for inferring patterns leading to drought,
based on a set of rules derived from indigenous knowledge of the local people
on drought. Figure 2 depicts the overview of the middleware architecture. The
domain of this particular case study is Free State Province, South Africa - an
ongoing research project by AfriCRID4 , Department of Information Technology,
Central University of Technology, Free State.

Fig. 1: The semantic middleware integration framework
Fig. 2: Overview of the middleware
architecture

5 Results and Discussion
The study is expected to produce a semantic based data integration middleware that semantically represents and integrates heterogeneous data sources
with indigenous knowledge based on a unified ontology for an accurate IoTbased drought forecasting system. With more integrated comprehensive services
that are based on semantic interoperability, our approach makes a unique contribution towards improving the accuracy of drought prediction and forecasting
systems.
4

http://africrid.com/

Semantics-Based Data Integration Middleware

5

References
1. D. Chester. Natural hazards by ea bryant. cambridge university press, 1991. no. of
pages: 294. price:40(hardback); 14.95 (paperback). isbn 0 521 37295 x (hardback);
0 521 37889 3 (paperback), 1993.
2. A. K. Mishra and V. P. Singh. A review of drought concepts. Journal of Hydrology,
391(1):202-216, 2010.
3. D. J. Peuquet and N. Duan. An event-based spatiotemporal data model (estdm)
for temporal analysis of geographical data. International journal of geographical
information systems, 9(1):7-24, 1995.
4. F. Mugabe, C. Mubaya, D. Nanja, P. Gondwe, A. Munodawafa, E. Mutswangwa,
I. Chagonda, P. Masere, J. Dimes, and C. Murewi. Use of indigenous knowledge
systems and scientific methods for climate forecasting in southern zambia and north
western zimbabwe. Zimbabwe Journal of Technological Sciences, 1(1):19-30, 2010.
5. M. Masinde and A. Bagula. Itiki: bridge between african indigenous knowledge and
modern science of drought prediction. Knowledge Management for Development
Journal, 7(3):274-290, 2011.
6. P. Sillitoe. The development of indigenous knowledge: a new applied anthropology
1. Current anthropology, 39(2):223-252, 1998.
7. Akanbi, Adeyinka K., and Muthoni Masinde. ”Towards Semantic Integration of
Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting.”
Proceedings of the Doctoral Symposium of the 16th International Middleware Conference. ACM, 2015.

