Intelligent computing based forecasting of deforestation using fire alerts: a deep learning approach

Jamshed, M. A., Theodorou, C., Kalsoom, T., Anjum, N., Abbasi, Q. H. and Ur-Rehman, M. (2022) Intelligent computing based forecasting of deforestation using fire alerts: a deep learning approach. Physical Communication, 55, 101941. (doi: 10.1016/j.phycom.2022.101941)

Abstract

Deforestation is depletion of the forest cover and degradation in forest quality mainly through repeated fires, over-exploitation, and diseases. In a forest ecosystem, occurrence of wildfires is a natural phenomena. The curse of global warming and man-made interventions have made the wildfires increasingly extreme and widespread. Though, extremely challenging due to rapidly changing climate, accurate prediction of these fire events can significantly improve forestation worldwide. In this paper, we have addressed this issue by proposing a deep learning (DL) framework using long short term memory (LSTM) model. The proposed mechanism accurately forecasts weekly fire alerts and associated burnt area (ha) utilizing historical fire data provided by GLOBAL FOREST WATCH. Pakistan is taken as a case study since its deforestation rate is among the highest in the world while having one of the lowest forest covers. Number of epochs, dense layers, hidden layers and hidden layer units are varied to optimize the model for high estimation accuracy and low root mean square error (RMSE). Simulation results show that the proposed method can predict the forest fire occurrences with 95% accuracy by employing a suitable hyperparameter tuning.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ur Rehman, Dr Masood and Jamshed, Dr Muhammad Ali and Abbasi, Dr Qammer
• Authors: Jamshed, M. A., Theodorou, C., Kalsoom, T., Anjum, N., Abbasi, Q. H., and Ur-Rehman, M.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Physical Communication
• Publisher: Elsevier
• ISSN: 1874-4907
• ISSN (Online): 1876-3219
• Published Online: 05 November 2022
• Copyright Holders: Copyright © 2022 Elsevier B.V.
• First Published: First published in Physical Communication 55: 101941
• Publisher Policy: Reproduced in accordance with the publisher copyright policy