Estimation of ground water level (GWL) for tropical peatland forest using machine learning
The tropical area has a large area of peatland, which is an important ecosystem that is regarded as home by millions of people, plants and animals. However, the dried-up and degraded peatland becomes extremely easy to burn, and in case of fire, it will further release transboundary haze. In order to...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/111527/ http://psasir.upm.edu.my/id/eprint/111527/1/Estimation_of_Ground_Water_Level_GWL_for_Tropical_Peatland_Forest_Using_Machine_Learning.pdf |
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| author | Li, Lu Sali, Aduwati Liew, Jiun Terng Saleh, Nur Luqman Syed Ahmad, Sharifah Mumtazah Mohd Ali, Azizi Nuruddin, Ahmad Ainuddin Amir Aziz, Nurizana Sitanggang, Imas Sukaesih Syaufina, Lailan Nurhayati, Ati Dwi Nishino, Hisanori Asai, Nobuyuki |
| author_facet | Li, Lu Sali, Aduwati Liew, Jiun Terng Saleh, Nur Luqman Syed Ahmad, Sharifah Mumtazah Mohd Ali, Azizi Nuruddin, Ahmad Ainuddin Amir Aziz, Nurizana Sitanggang, Imas Sukaesih Syaufina, Lailan Nurhayati, Ati Dwi Nishino, Hisanori Asai, Nobuyuki |
| author_sort | Li, Lu |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The tropical area has a large area of peatland, which is an important ecosystem that is regarded as home by millions of people, plants and animals. However, the dried-up and degraded peatland becomes extremely easy to burn, and in case of fire, it will further release transboundary haze. In order to protect the peatland, an improved tropical peatland fire weather index (FWI) system is proposed by combining the ground water level (GWL) with the drought code (DC). In this paper, LoRa based IoT system for peatland management and detection was deployed in Raja Musa Forest Reserve (RMFR) in Kuala Selangor, Malaysia. Then, feasibility of data collection by the IoT system was verified by comparing the correlation between the data obtained by the IoT system and the data from Malaysian Meteorological Department (METMalaysia). An improved model was proposed to apply the ground water level (GWL) for Fire Weather Index (FWI) formulation in Fire Danger Rating System (FDRS). Specifically, Drought Code (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 data monitored by the IoT system has a high correlation of 0.8 with the data released by METMalaysia, and the Mean Squared Error (MSE) between the predicted and real values of the ground water level of the two sensor nodes deployed through neural network machine learning are 0.43 and 12.7 respectively. This finding reveals the importance and feasibility of the ground water level used in the prediction of the tropical peatland fire weather index system, which can be used to the maximum extent to help predict and reduce the fire risk of tropical peatland. |
| first_indexed | 2025-11-15T14:09:04Z |
| format | Article |
| id | upm-111527 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:09:04Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1115272024-07-29T08:44:22Z http://psasir.upm.edu.my/id/eprint/111527/ Estimation of ground water level (GWL) for tropical peatland forest using machine learning Li, Lu Sali, Aduwati Liew, Jiun Terng Saleh, Nur Luqman Syed Ahmad, Sharifah Mumtazah Mohd Ali, Azizi Nuruddin, Ahmad Ainuddin Amir Aziz, Nurizana Sitanggang, Imas Sukaesih Syaufina, Lailan Nurhayati, Ati Dwi Nishino, Hisanori Asai, Nobuyuki The tropical area has a large area of peatland, which is an important ecosystem that is regarded as home by millions of people, plants and animals. However, the dried-up and degraded peatland becomes extremely easy to burn, and in case of fire, it will further release transboundary haze. In order to protect the peatland, an improved tropical peatland fire weather index (FWI) system is proposed by combining the ground water level (GWL) with the drought code (DC). In this paper, LoRa based IoT system for peatland management and detection was deployed in Raja Musa Forest Reserve (RMFR) in Kuala Selangor, Malaysia. Then, feasibility of data collection by the IoT system was verified by comparing the correlation between the data obtained by the IoT system and the data from Malaysian Meteorological Department (METMalaysia). An improved model was proposed to apply the ground water level (GWL) for Fire Weather Index (FWI) formulation in Fire Danger Rating System (FDRS). Specifically, Drought Code (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 data monitored by the IoT system has a high correlation of 0.8 with the data released by METMalaysia, and the Mean Squared Error (MSE) between the predicted and real values of the ground water level of the two sensor nodes deployed through neural network machine learning are 0.43 and 12.7 respectively. This finding reveals the importance and feasibility of the ground water level used in the prediction of the tropical peatland fire weather index system, which can be used to the maximum extent to help predict and reduce the fire risk of tropical peatland. Institute of Electrical and Electronics Engineers 2022 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/111527/1/Estimation_of_Ground_Water_Level_GWL_for_Tropical_Peatland_Forest_Using_Machine_Learning.pdf Li, Lu and Sali, Aduwati and Liew, Jiun Terng and Saleh, Nur Luqman and Syed Ahmad, Sharifah Mumtazah and Mohd Ali, Azizi and Nuruddin, Ahmad Ainuddin and Amir Aziz, Nurizana and Sitanggang, Imas Sukaesih and Syaufina, Lailan and Nurhayati, Ati Dwi and Nishino, Hisanori and Asai, Nobuyuki (2022) Estimation of ground water level (GWL) for tropical peatland forest using machine learning. IEEE Access, 10. pp. 126180-126187. ISSN 21693536 https://ieeexplore.ieee.org/document/9967973/ 10.1109/access.2022.3225906 |
| spellingShingle | Li, Lu Sali, Aduwati Liew, Jiun Terng Saleh, Nur Luqman Syed Ahmad, Sharifah Mumtazah Mohd Ali, Azizi Nuruddin, Ahmad Ainuddin Amir Aziz, Nurizana Sitanggang, Imas Sukaesih Syaufina, Lailan Nurhayati, Ati Dwi Nishino, Hisanori Asai, Nobuyuki Estimation of ground water level (GWL) for tropical peatland forest using machine learning |
| title | Estimation of ground water level (GWL) for tropical peatland forest using machine learning |
| title_full | Estimation of ground water level (GWL) for tropical peatland forest using machine learning |
| title_fullStr | Estimation of ground water level (GWL) for tropical peatland forest using machine learning |
| title_full_unstemmed | Estimation of ground water level (GWL) for tropical peatland forest using machine learning |
| title_short | Estimation of ground water level (GWL) for tropical peatland forest using machine learning |
| title_sort | estimation of ground water level (gwl) for tropical peatland forest using machine learning |
| url | http://psasir.upm.edu.my/id/eprint/111527/ http://psasir.upm.edu.my/id/eprint/111527/ http://psasir.upm.edu.my/id/eprint/111527/ http://psasir.upm.edu.my/id/eprint/111527/1/Estimation_of_Ground_Water_Level_GWL_for_Tropical_Peatland_Forest_Using_Machine_Learning.pdf |