Forest Fire Prediction Using Machine Learning Techniques - 1st to 5th Authors - Abstract

Aug 2, 2026

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Forest fires pose a significant threat to ecosystems, biodiversity, and human livelihoods worldwide. Early detection and accurate prediction are essential for timely intervention and effective mitigation. The study applies machine learning techniques to forecast forest fire occurrence and severity by using features including weather conditions, topography, vegetation density, and historical fire data. Models such as decision trees, random forests, support vector machines, and neural networks are evaluated with accuracy, precision, recall, and F1-score.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

INTRODUCTION

Traditional methods and limitations

Machine learning approaches and data sources

Remote sensing and real-time sensor integration

LITERATURE REVIEW

Data sources for training

Model development approaches

Forest Fire Prediction Using Machine Learning Techniques 1st Rahul Kumar Paswan MCA Research Scholar, CS & IT Department, Kalinga University, Raipur, India [paswanrahulkumar2001@gmail.com](paswanrahulkumar2001@gmail.com) [2nd](2nd Prof)[ Prof](2nd Prof). Dr. Asha Ambhaikar Professor, CS & IT Department, Kalinga University, Raipur, India [asha.ambhaikar@kalingauniversity.ac.in](asha.ambhaikar@kalingauniversity.ac.in) 3rd Ravitesh Kumar MCA Research Scholar, CS & IT Department, Kalinga University, Raipur, India [raviteshkumar12345678@gmail.com](raviteshkumar12345678@gmail.com) 4th Naman Bedre MCA Research Scholar, CS & IT Department, Kalinga University, Raipur, India [namanbedre68@gmail.com](namanbedre68@gmail.com) 5thAbinash Kumar Singh MCA Research Scholar, CS & IT Department, Kalinga University, Raipur, India [Singabinash79@gmail.com](Singabinash79@gmail.com) Abstract: Forest fires pose a significant threat to ecosystems, biodiversity, and human livelihoods worldwide. Early detection and accurate prediction of forest fires are essential for timely intervention and mitigation efforts. In recent years, machine learning techniques have emerged as powerful tools for forest fire prediction, leveraging diverse data sources and advanced algorithms to improve predictive accuracy. This study explores the application of machine learning techniques in forest fire prediction, utilizing features such as weather conditions, topography, vegetation density, and historical fire data. Various machine learning models, including decision trees, random forests, support vector machines, and neural networks, are employed to develop predictive models capable of forecasting forest fire occurrence and severity. The study evaluates the performance of these models using metrics such as accuracy, precision, recall, and F1-score, and compares their effectiveness in different environmental settings. Additionally, the research investigates the integration of remote sensing data and real-time sensor networks for enhancing the spatial and temporal resolution of forest fire prediction models. Through comprehensive experimentation and validation, this study aims to contribute to the development of robust and reliable systems for forest fire prediction, ultimately aiding in the preservation of natural ecosystems and the protection of human lives and property. Keywords: Forest Fire Prediction, Machine Learning, Early Detection, Predictive Modeling, Weather Conditions, Topography, Vegetation Density, Remote Sensing, Sensor Networks. INTRODUCTION Forest fires represent a significant environmental hazard, causing extensive damage to ecosystems, biodiversity, and human infrastructure globally. Timely detection and accurate prediction of forest fires are critical for effective fire management, enabling rapid response and mitigation efforts to minimize their impact. Traditional methods of forest fire prediction often rely on manual observation, historical data analysis, and meteorological forecasting, which may be limited in their predictive capabilities and response times. In recent years, the advent of machine learning techniques has revolutionized the field of forest fire prediction, offering powerful tools to harness the wealth of available data and improve predictive accuracy. Machine learning algorithms can analyze diverse datasets, including weather conditions, topographical features, vegetation density, historical fire records, and satellite imagery, to identify patterns and trends associated with forest fire occurrence. By leveraging these data sources, machine learning models can forecast the likelihood, severity, and spatial extent of forest fires with greater precision and efficiency. This study explores the application of machine learning techniques in forest fire prediction, aiming to develop robust predictive models capable of early detection and accurate forecasting. By examining various machine learning algor

FAQs

Which data features are used to predict forest fires in this study?

The study uses weather conditions, topography, vegetation density, and historical fire data, along with remote sensing and satellite-related information to support prediction.

What machine learning models are compared for forest fire prediction?

Decision trees, random forests, support vector machines, and neural networks are employed to build predictive models for both occurrence and severity.

How is model performance evaluated?

Performance is assessed using metrics such as accuracy, precision, recall, and F1-score, and compared across different environmental settings.

Aug 2, 2026

Upload by Levi