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Forest Fire

Forest Fire

Price : 8000

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Course Duration
Approx 10

Course Price
₹ 8000

Course Level
Moderate

Course Content

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.

 

Forest Fire

Hardware Requirement:-

      System: Pentium IV 2.4 GHz.

      Hard Disk: 500 GB.

      Ram: 4 GB.

      Any desktop / Laptop system with above configuration or higher level.

 

Software Requirements:-

      Operating system : Windows XP / 7

      Coding Language :Python

      Interpreter  :Python 3.6

      IDE             : Jupyter notebook

 

      ML APIS         :Sklearn, numpy, pandas, matplotlib, keras, tensorflow, flask.

 

       The proposed system forest fire risk prediction mechanism, based on machine learning techniques and algorithms. The machine learning algorithms used like SVM , logistic regression and decision tree. The results demonstrates the ability to predict forest fire risk with a limited amount of data and has shown that model can be used for prediction of fire risk with a very high accuracy. 

 

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