Peatlands Monitoring in Malaysia with IoT Systems: Preliminary Experimental Results

Sali, Aduwati; Ali, Azizi Mohd; Ali, Borhanuddin Mohd; Ahmad, Sharifah Mumtazah Syed; Liew, Jiun Terng; Saleh, Nur Luqman; Nuruddin, Ahmad Ainuddin; Razali, Sheriza Mohd; Nsaif, Ibtisam G.; Ramli, Nordin; Mohamad, Hafizal; Terada, Naomi

doi:10.1007/978-3-030-68133-3_23

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…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.…”

Section: Data Preparationmentioning

confidence: 99%

Prediction of Peatlands Forest Fires in Malaysia Using Machine Learning

Sali

Noordin

et al. 2023

Forests

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The occurrence of fires in tropical peatlands poses significant threats to their ecosystems. An Internet of Things (IoT) system was developed to measure and collect fire risk factors in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, to address this issue. In this paper, neural networks with different layers were employed to predict peatland forests’ Fire Weather Index (FWI). The neural network models used two sets of input parameters, consisting of four and nine fire factors. The predicted FWI values were compared with actual values obtained from the Malaysian meteorological department. The findings revealed that the five-layer neural network outperformed others in both the four-input and nine-input models. Specifically, the nine-input neural network achieved a mean square error (MSE) of 1.116 and a correlation of 0.890, surpassing the performance of the four-input neural network with the MSE of 1.537 and the correlation of 0.852. These results hold significant research and practical implications for precise peatland fire prevention, control, and the formulation of preventive measures.

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Section: Data Preparationmentioning

confidence: 99%

Prediction of Peatlands Forest Fires in Malaysia Using Machine Learning

Sali

Noordin

et al. 2023

Forests

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“…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.…”

Section: Iot System For Peatland Forest Managementmentioning

confidence: 99%

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

Sali

Noordin

et al. 2023

Fire

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Peatland refers to the peat soil and wetland biological environment growing on the surface. However, unexpected fires in peatlands frequently have brought severe greenhouse gas emissions and transboundary haze to Southeast Asia. To alleviate this issue, this paper first establishes an Internet of Things (IoT) system for peatland monitoring and management in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, and proposes a more efficient and low-complexity model for calculating the Duff Moisture Code (DMC) in peatland forests using groundwater level (GWL) and relative humidity. The feasibility of the IoT system is verified by comparing its data with those published by Malaysian Meteorological Department (METMalaysia). The proposed Linear_DMC Model and Linear_Mixed_DMC Model are compared with the Canadian Fire Weather Index (FWI) model, and their performance is evaluated using IoT measurement data and actual values published by METMalaysia. The results show that the correlation between the measured data of the IoT system and the data from METMalaysia within the same duration is larger than 0.84, with a mean square error (MSE) of 2.56, and a correlation of 0.91 can be achieved between calculated DMC using the proposed model and actual values. This finding is of great significance for predicting peatland forest fires in the field and providing the basis for fire prevention and decision making to improve disaster prevention and reduction.

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“…Instead of relying on human observations, Sali et al [92] adopted an Internet of Things (IoT) approach to monitor the condition of the Raja Musa Forest Reserve, Selangor. By deploying an IoT monitoring system, real-time data including soil temperature, soil humidity, water level, wind speed, rain precipitation, ambient humidity, and ambient temperature information could be collected.…”

Section: Other Efforts Associated With Forest Firementioning

confidence: 99%

A Review of Forest Fire Combating Efforts, Challenges and Future Directions in Peninsular Malaysia, Sabah, and Sarawak

Chew

Yin

Pang

et al. 2022

Forests

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The land surface of Malaysia mostly constitutes forest cover. For decades, forest fires have been one of the nation’s most concerning environmental issues. With the advent of machine learning, many studies have been conducted to resolve forest fire issues. However, the findings and results have been very case-specific. Most experiments have focused on particular regions with independent methodology settings, which has hindered the ability of others to reproduce works. Another major challenge is lack of benchmark datasets in this domain, which has made benchmark comparisons almost impossible to conduct. To our best knowledge, no comprehensive review and analysis have been performed to streamline the research direction for forest fires in Malaysia. Hence, this paper was aimed to review all works aimed to combat forest fire issues in Malaysia from 1989 to 2021. With the proliferation of publicly accessible satellite data in recent years, a new direction of utilising big data platforms has been postulated. The merit of this approach is that the methodology and experiments can be reproduced. Thus, it is strongly believed that the findings and analysis shown in this paper will be useful as a baseline to propagate research in this domain.

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Peatlands Monitoring in Malaysia with IoT Systems: Preliminary Experimental Results

Cited by 5 publications

References 9 publications

Prediction of Peatlands Forest Fires in Malaysia Using Machine Learning

Prediction of Peatlands Forest Fires in Malaysia Using Machine Learning

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

A Review of Forest Fire Combating Efforts, Challenges and Future Directions in Peninsular Malaysia, Sabah, and Sarawak

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