Development of an intelligent environmental knowledge system for sustainable agricultural decision support
Dutta, R and Morshed, A and Aryal, J and D'Este, C and Das, A, Development of an intelligent environmental knowledge system for sustainable agricultural decision support, Environmental Modelling and Software, 52 pp. 264-272. ISSN 1364-8152 (2014) [Refereed Article]
The purpose of this research was to develop a knowledge recommendation architecture based on unsupervised
machine learning and unified resource description framework (RDF) for integrated environmental
sensory data sources. In developing this architecture, which is very useful for agricultural
decision support systems, we considered web based large-scale dynamic data mining, contextual
knowledge extraction, and integrated knowledge representation methods. Five different environmental
data sources were considered to develop and test the proposed knowledge recommendation framework
called Intelligent Environmental Knowledgebase (i-EKbase); including Bureau of Meteorology SILO,
Australian Water Availability Project, Australian Soil Resource Information System, Australian National
Cosmic Ray Soil Moisture Monitoring Facility, and NASA’s Moderate Resolution Imaging Spectroradiometer.
Unsupervised clustering techniques based on Principal Component Analysis (PCA), Fuzzy-CMeans
(FCM) and Self-organizing map (SOM) were used to create a 2D colour knowledge map representing
the dynamics of the i-EKbase to provide "prior knowledge" about the integrated knowledgebase.
Prior availability of recommendations from the knowledge base could potentially optimize the accessibility
and usability issues related to big data sets and minimize the overall application costs. RDF representation
has made i-EKbase flexible enough to publish and integrate on the Linked Open Data cloud.
This newly developed system was evaluated as an expert agricultural decision support for sustainable
water resource management case study in Australia at Tasmania with promising results.
feature, semantic matching, knowledge integration, i-EKbase, linked open data cloud