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Engineering and Technology OPEN ACCESS

AI-Driven Strategies for Reducing Deforestation

Rakibul Hasan
Master Of Business Administration (Information Technology), Westcliff University, California 90020, USA
Syeda Farjana Farabi
Doctorate In Business Administration, Westcliff University, California 90020, USA
Md Kamruzzaman
Dba In Business Intelligence And Data Analytics, Westcliff University, California 90020, USA
Md Khokan BHUYAN
Masters Of Science In Engineering Management, Westcliff University, California 90020, USA
Sadia Islam Nilima
Doctorate In Business Administration, International American University, California 90004, USA
Atia Shahana
Masters Of Science, National University, Bangladesh
DOI Crossref DOI 10.37547/tajet/Volume06Issue06-02
tajet 2024
VOL. 6 / NO. 06 JUNE
FULL TEXT PP. 6-20
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VOLUME 6
ISSUE 06
YEAR 2024
PAGES 6-20
RESEARCH OVERVIEW

Abstract

Recent advancements in data science, coupled with the revolution in digital and satellite technology, have catalyzed the potential for artificial intelligence (AI) applications in forestry and wildlife sectors. Recognizing the critical importance of addressing land degradation and promoting regeneration for climate regulation, ecosystem services, and population well-being, there is a pressing need for effective land use planning and interventions. Traditional regression approaches often fail to capture underlying drivers' complexity and nonlinearity. In response, this research investigates the efficacy of AI in monitoring, predicting, and managing deforestation and forest degradation compared to conventional methods, with a goal to bolster global forest conservation endeavors. Employing a fusion of satellite imagery analysis and machine learning algorithms, such as convolutional neural networks and predictive modelling, the study focuses on key forest regions, including the Amazon Basin, Central Africa, and Southeast Asia. Through the utilization of these AI-driven strategies, critical deforestation hotspots have been successfully identified with an accuracy surpassing 85%, markedly higher than traditional methods. This breakthrough underscores the transformative potential of AI in enhancing the precision and efficiency of forest conservation measures, offering a formidable tool for combating deforestation and degradation on a global scale.

RESEARCH TOPICS

Keywords

Fraud Detection Traditional fraud detection Artificial Intelligence Banking Security Risk Management

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