Page 15 - Preliminary Analysis Towards a Standardized Readiness Framework - Interim Report

Preliminary Analysis Towards a Standardized Readiness Framework

a LoRa-based IoT system is used, and sensors such as soil sensors, piezometer sensors, water
level sensors, and weather sensors are used, with the expectation that integral meteorological
information could be detected. All the data mentioned above could be cross-checked with the
ones used by the Malaysian Meteorological Department (METMalaysia), which means that the
data collected by the IoT system is authentic and ready to be processed.
In addition, the team proposed an improved model to apply the GWL for the FWI formulation
in the Fire Danger Rating System (FDRS). Specifically, DC is formulated using GWL, instead of
temperature and rain in the existing model. From the GWL aggregated from the IoT system,
the parameter is predicted using machine learning based on a neural network. The results
show that the DC calculated from the IoT system has a high correlation with the data released
by METMAlaysia. This shows that DC can be calculated using predicted GWL.

The solution has been deployed in Raja Musa Forest Reserve (RMFR) in Kuala Selangor,
Malaysia. Deployment in a natural environment proves the effectiveness and efficiency of the
whole system. It is desirable to extend the project by measuring carbon emissions from the
peatland forest and how net zero can be achieved by managing the peatland better using IoT
technology and community-based management.

Sensor data collected in real-time as part of the use case is stored in an integrated cloud server
where all members can access and analyze the data, enabling an ecosystem for all developers,
analysts, and designers to learn and reuse.

Fig 2 - Instances of Readiness Factors in Case Study-1

3.2 Case Study-2: AI-based Frontend with Multimodal Backend Data
Aggregation

This case study aggregates multiple types of data from varied sources and maps them together
to form actionable insights for potential users. These insights may be offered as questions/
answers over the chat interface. Context sensitivity and local customization are important for
these types of use cases. Dynamic update of data in the backend should result in corresponding

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