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Estimation of Ground Water Level (GWL) for Tropical Peatland Forest Using Machine Learning

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dc.contributor.authorLi L.en_US
dc.contributor.authorSali A.en_US
dc.contributor.authorLiew J.T.en_US
dc.contributor.authorSaleh N.L.en_US
dc.contributor.authorAhmad S.M.S.en_US
dc.contributor.authorAli A.M.en_US
dc.contributor.authorNuruddin A.A.en_US
dc.contributor.authorAziz N.A.en_US
dc.contributor.authorSitanggang I.S.en_US
dc.contributor.authorSyaufina L.en_US
dc.contributor.authorNurhayati A.D.en_US
dc.contributor.authorNishino H.en_US
dc.contributor.authorAsai N.en_US
dc.contributor.authorid58017051400en_US
dc.contributor.authorid22981598500en_US
dc.contributor.authorid57209739798en_US
dc.contributor.authorid57198797134en_US
dc.contributor.authorid24721182400en_US
dc.contributor.authorid57208220348en_US
dc.contributor.authorid56287276100en_US
dc.contributor.authorid56704507600en_US
dc.contributor.authorid35230685400en_US
dc.contributor.authorid16319669700en_US
dc.contributor.authorid57191333787en_US
dc.contributor.authorid58017267800en_US
dc.contributor.authorid58018153200en_US
dc.date.accessioned2023-05-29T09:39:01Z
dc.date.available2023-05-29T09:39:01Z
dc.date.issued2022
dc.descriptionArtificial 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; Droughten_US
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2022.3225906
dc.identifier.epage126187
dc.identifier.scopus2-s2.0-85144016425
dc.identifier.spage126180
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85144016425&doi=10.1109%2fACCESS.2022.3225906&partnerID=40&md5=8041ad4b50707584df735d5f3048940f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27049
dc.identifier.volume10
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleEstimation of Ground Water Level (GWL) for Tropical Peatland Forest Using Machine Learningen_US
dc.typeArticleen_US
dspace.entity.typePublication
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