Estimation of Ground Water Level (GWL) for Tropical Peatland Forest Using Machine Learning
Artificial intelligence; Fires; Forestry; Groundwater; Internet of things; Mean square error; Sensor nodes; Tropics; Water levels; Fire weather index; Forest reserves; Ground water level; IoT system; Machine-learning; Malaysia; Neural-networks; Peat land; Trans-boundary; Weather index systems; Droug...
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2023
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my.uniten.dspace-270492023-05-29T17:39:01Z Estimation of Ground Water Level (GWL) for Tropical Peatland Forest Using Machine Learning Li L. Sali A. Liew J.T. Saleh N.L. Ahmad S.M.S. Ali A.M. Nuruddin A.A. Aziz N.A. Sitanggang I.S. Syaufina L. Nurhayati A.D. Nishino H. Asai N. 58017051400 22981598500 57209739798 57198797134 24721182400 57208220348 56287276100 56704507600 35230685400 16319669700 57191333787 58017267800 58018153200 Artificial intelligence; Fires; Forestry; Groundwater; Internet of things; Mean square error; Sensor nodes; Tropics; Water levels; Fire weather index; Forest reserves; Ground water level; IoT system; Machine-learning; Malaysia; Neural-networks; Peat land; Trans-boundary; Weather index systems; Drought 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. � 2013 IEEE. Final 2023-05-29T09:39:01Z 2023-05-29T09:39:01Z 2022 Article 10.1109/ACCESS.2022.3225906 2-s2.0-85144016425 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144016425&doi=10.1109%2fACCESS.2022.3225906&partnerID=40&md5=8041ad4b50707584df735d5f3048940f https://irepository.uniten.edu.my/handle/123456789/27049 10 126180 126187 Institute of Electrical and Electronics Engineers Inc. Scopus |
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Artificial intelligence; Fires; Forestry; Groundwater; Internet of things; Mean square error; Sensor nodes; Tropics; Water levels; Fire weather index; Forest reserves; Ground water level; IoT system; Machine-learning; Malaysia; Neural-networks; Peat land; Trans-boundary; Weather index systems; Drought |
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58017051400 |
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58017051400 Li L. Sali A. Liew J.T. Saleh N.L. Ahmad S.M.S. Ali A.M. Nuruddin A.A. Aziz N.A. Sitanggang I.S. Syaufina L. Nurhayati A.D. Nishino H. Asai N. |
| format |
Article |
| author |
Li L. Sali A. Liew J.T. Saleh N.L. Ahmad S.M.S. Ali A.M. Nuruddin A.A. Aziz N.A. Sitanggang I.S. Syaufina L. Nurhayati A.D. Nishino H. Asai N. |
| spellingShingle |
Li L. Sali A. Liew J.T. Saleh N.L. Ahmad S.M.S. Ali A.M. Nuruddin A.A. Aziz N.A. Sitanggang I.S. Syaufina L. Nurhayati A.D. Nishino H. Asai N. Estimation of Ground Water Level (GWL) for Tropical Peatland Forest Using Machine Learning |
| author_sort |
Li L. |
| title |
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_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_sort |
estimation of ground water level (gwl) for tropical peatland forest using machine learning |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806426280002846720 |
| score |
13.214268 |