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Forest Fire Prediction using machine learning

Forest Fire Prediction

Price : 10000

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Course Duration
Approx 10

Course Price
₹ 10000

Course Level

Course Content

Abstract :

Forest fire prediction plays a major role in resource allocation, mitigation and recovery efforts. Forest wildfire risk is increasing in the western United States. In the past five decades, large wildfire frequency and the area destroyed have risen by more than four and six times, respectively. In wildfire risk assessments, forest dryness is an important predictor of wildfire ignition and spread is estimated using meteorological indicators such as prior precipitation and temperature. Test the sensitivity of wildfire occurrence and size to forest dryness. In the process, we will quantify the value of these forest dryness maps for wildfire risk forecasting. A novel forest fire risk prediction algorithm, based on support vector machines, logistic regression and decision tree is presented. The results demonstrate the ability to predict forest fire risk with good accuracy.

So we proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Fore, Decision Tree and Naïve Bayes to predict percentage of fire occurrence based on different parameters like temperature, humidity and oxygen data entered by the user in the front end.

 


 

Introduction:

Forest fire is a common natural world phenomenon. Every year millions of hectares of forests in the world are destroyed. Forest fire is one of the major environmental concern that affects the preservation of forests, resulting in economical and ecological damage that causes human suffering. Forest fire prediction constitutes a significant component of forest fire management. It plays a major role in resource allocation, mitigation and recovery efforts. This paper presents a description and analysis of forest fire prediction methods based on machine learning techniques. A novel forest fire risk prediction algorithm, based on support vector machines, decision tree and logistic regression is presented. The algorithm depends on previous weather conditions in order to predict the fire hazard level of a day. The implementation of the algorithm using data from dataset to accurately predict the hazard of fire occurrence.

 

Forest fires are an integral part of many terrestrial ecosystems such as boreal forests, temperate forests, Mediterranean ecosystems, savannas and grasslands, among others. Fires in the Mediterranean basin account for a significant percentage of total fires occurring worldwide . Forest fire prediction, prevention and management measures have become increasingly important. Systems for forest firedanger prediction represent an essential tool to predict forest fire risks, back up the forest fire monitoring and extinction phase, and to assist in the fire control planning and resource allocation.

 

 

 

So we proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree and Naïve Bayes to predict percentage of fire occurrence based on different parameters like temperature, humidity and oxygen data entered by the user in the front end.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Objective:

The main aim of this project to predict the percentage of fire occurrence using machine learning techniques and algorithms like like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree and Naïve Bayes to predict percentage of fire occurrence based on different parameters like temperature, humidity and oxygen data entered by the user in the front end.


 

Problem Statement

Forest fires are a major environmental issue, Forest fire cause a significant environmental damage while threatening human lives. So Prediction of forest fire occurrence in advance plays a major to take appropriate precautions.

 

 


 

Proposed System:

 

We proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree and Naïve Bayes to predict percentage of fire occurrence based on different parameters like temperature, humidity and oxygen data entered by the user in the front end.


 

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