  The forests are significant assets for every country. When it gets destroyed,
it may negatively impact the environment, and forest fire is one of the primary
causes. Fire weather indices are widely used to measure fire danger and are
used to issue bushfire warnings. It can also be used to predict the demand for
emergency management resources. Sensor networks have grown in popularity in
data collection and processing capabilities for a variety of applications in
industries such as medical, environmental monitoring, home automation etc.
Semantic sensor networks can collect various climatic circumstances like wind
speed, temperature, and relative humidity. However, estimating fire weather
indices is challenging due to the various issues involved in processing the
data streams generated by the sensors. Hence, the importance of forest fire
detection has increased day by day. The underlying Semantic Sensor Network
(SSN) ontologies are built to allow developers to create rules for calculating
fire weather indices and also the convert dataset into Resource Description
Framework (RDF). This research describes the various steps involved in
developing rules for calculating fire weather indices. Besides, this work
presents a Web-based mapping interface to help users visualize the changes in
fire weather indices over time. With the help of the inference rule, it
designed a decision support system using the SSN ontology and query on it
through SPARQL. The proposed fire management system acts according to the
situation, supports reasoning and the general semantics of the open-world
followed by all the ontologies
