Sustainable Peatland Management with IoT and Data Analytics

Abstract

Peatland is important to rural communities' livelihood due to its potential for aquaculture and agriculture. Nonetheless, human activities such as slash-and-burn can greatly increase forest fire risk, which can release a great amount of greenhouse gases and carbon dioxide into the atmosphere. To sustainably manage and restore peatlands, the Internet of Things (IoT) system can incorporate with Cyber-Physical System (CPS) for peatland management. In this study, an IoT system is deployed in the peatland to monitor the ground water level (GWL) and upload it to the server for the machine learning (ML) process. The trend of GWL will be modelled, and the CPS using the developed ML model will control the peatland rewatering process. As a result, the peatland condition can be monitored in real-time, and the risk of forest fire can be mitigated through rewatering automation before the GWL drops to a critical level.

Modeling of Evaporation Rate for Peatland Fire Prevention Using Internet of Things (IoT) System

“…This means that our IoT system can continuously monitor and display weather parameters such as rainfall, temperature, and humidity, as well as GWL data from the ground sensor Figure 4 depicts the integration of groundwater level (GWL) into the Fire Weather Index (FWI) system. Previous studies have confirmed the viability of using GWL in place of temperature and rainfall to compute the Drought Code (DC) in the FWI system [28]. Precisely, the Duff Moisture Code (DMC) is calculated using GWL and relative humidity, as opposed to temperature, rainfall, and relative humidity in the original Canadian FWI system (refer to Figure 3).…”

“…Integrating IoT technology into the study of peatlands holds great promise for advancing our understanding of these ecosystems and mitigating the risk of environmental disasters caused by forest fires. Moreover, the system has been used, and many research results based on the IoT system have been published [27][28][29]. To facilitate the smooth flow of groundwater within the pipeline, a small hole is incorporated while ensuring that the gap is not too large to prevent the piezometer from being obstructed by soil and stones.…”

Prediction of Peatlands Forest Fires in Malaysia Using Machine Learning

“…Moreover, it was put into use on 17 January 2020. More information based on the IoT system has been described in detail in our team's previous publication [19][20][21]. The histogram of the data used in this paper is shown in Figure 2.…”