relation: https://khub.utp.edu.my/scholars/17515/
title: Forecasting of Fires Hotspot in Tropical Region Using LSTM Algorithm Based on Satellite Data
creator: Kadir, E.A.
creator: Kung, H.T.
creator: Rosa, S.L.
creator: Sabot, A.
creator: Othman, M.
creator: Ting, M.
description: Raising of temperature due to global warming impacted to the number of fires hotspot globally, in tropical region some of the places a high risk such as wildfire. Fire in Indonesia is one of the big disasters because of forestry region and peat land that highly risk to wildfire especially in dry season. This research aims to do a forecasting number of fires hotspot in future time based on training data collected in previous years. Long Short-Term Memory (LSTM) algorithm applied in this forecasting, the advantages of LSTM model to forecast a timeseries data make the high prediction accuracy. Fires hotspot data was collected since year 2010 to 2021 with total number of datasets more than 700,000 hotspot and forecast for the future year 2022. Collected datasets analyzed use LSTM model divided into two classification which are training and test data with 70 and 30 respectively. Results shows a similar pattern of forecasting fires hotspot in 2022 compared into previous last 2 years which 2021 and 2020. Furthermore, to proof proposed algorithm is working fine then a forecast number of fire hotspot for year 2021 have been done which compared actual and forecasting data and percentage of error 4.56. LSTM Algorithm is one of the models suitable to use in data forecasting in high volume time series data. © 2022 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Kadir, E.A. and Kung, H.T. and Rosa, S.L. and Sabot, A. and Othman, M. and Ting, M.  (2022) Forecasting of Fires Hotspot in Tropical Region Using LSTM Algorithm Based on Satellite Data.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138489426&doi=10.1109%2fTENSYMP54529.2022.9864407&partnerID=40&md5=93bd9bb5a2aa01058c4d75fc676c6f22
relation: 10.1109/TENSYMP54529.2022.9864407
identifier: 10.1109/TENSYMP54529.2022.9864407