Orman yangınları çalışmalarında uygulanan makine öğrenmesi (ML) tekniklerine genel bir bakış

Year 2024, Volume: 12 Issue: 1, 1 - 9, 25.02.2024

Ali Bahadır Küçükarslan

https://doi.org/10.31195/ejejfs.1386306

Abstract

Dünya genelinde orman yangınlarının sıklığının artması, ciddi çevresel ve ekonomik zararlara neden olmaktadır ve bu durum, erken yangın tahmini ve tespiti için zorunlu bir ihtiyaç doğurmuştur. Bu çalışma, orman yangınlarını tahmin etme ve tespit etme konusunda makine öğrenimi tekniklerinin kullanımını incelemeyi amaçlamaktadır. Orman yangını tahmini için önerilen çeşitli teknolojiler ve teknikler üzerine kapsamlı bir inceleme yapılmıştır. Her bir makine öğrenimi algoritmasının artılarını ve eksilerini anlayarak, en etkili yaklaşımları belirlemek için özel bir vurgu yapılmıştır. Orman yangınlarını tahmin etmek için pek çok makine öğrenimi yöntemi bulunmasına rağmen, her birinin kendine has güçlü yönleri ve sınırlılıkları olduğu gözlemlenmiştir. Bazı teknikler, özel orman özelliklerine göre uyarlandığında, gelişmiş tahmin yetenekleri sergilemiştir. Makine öğrenimi (ML), orman yangını çalışmalarının ilerlemesinde kilit bir rol oynamaktadır. Ormanın özelliklerine ve verinin doğasına bağlı olarak en uygun ML tekniğini belirleyerek kullanmak, tahmin doğruluğunu önemli ölçüde artırabilir.

Keywords

Orman yangınları, makine öğrenimi, yapay zeka, risk değerlendirmesi

An overview of machine learning (ML) techniques applied to forest fire studies

Abstract

With the increasing frequency of forest fires globally, causing substantial environmental and economic damages, there is an imperative need for early fire prediction and detection. This study aims to examine the utility of machine learning techniques in predicting and identifying forest fires. A comprehensive review was conducted on various technologies and techniques proposed for forest fire prediction. Particular emphasis was placed on understanding the pros and cons of each machine learning algorithm, with an aim to identify the most effective approaches. It was observed that while numerous machine learning methods exist for forecasting forest fires, each possesses unique strengths and limitations. Some techniques, when tailored to specific forest characteristics, displayed enhanced predictive capabilities. Machine learning (ML) plays a pivotal role in advancing the field of forest fire studies. Identifying and utilizing the most suited ML technique, based on forest characteristics and the nature of data, can significantly augment prediction accuracy.

Keywords

Forest fires, machine learning, artificial intelligence, risk evaluation

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