Comparative Analysis of ML Algorithms for Drought for Prediction

Project Code :TCPGPY403

Objective

The main objective of this project is to perform future forecasting for drought prediction using traditional time series methods as well as latest algorithm too.

Abstract

Many droughts can be predicted up to a month in advance, and in rare cases it may be possible to predict drought conditions more than a year in advance. However, the complexity of Earth’s climate makes drought forecasting very difficult. To overcome these severe problems, the urgent need is to monitor or predict the drought severity of lands. Some droughts are easier to predict than others, and some still take us by surprise, but drought forecasting continues to improve as more data are collected and better models are produced. With continuous increase in the drought, most of the human life will get into panic situation. Now a days Artificial Intelligence is increasing its roots in every domain. So by using the subset machine learning from the Artificial Intelligence we are going to forecast the drought. Traditional time series analysis methods such as vector auto regression (Vector Auto Regression), arima (Auto Regressive Integrated Moving Averages) are implemented in this project. And also we implemented the most powerful and fully automated algorithm prophet which was developed by Facebook’s Data science team.

 KEYWORDS: VAR, ARIMA, prophet, drought..,

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

H/W SPECIFICATIONS:

  • Processor:I3/Intel Processor
  • RAM: 8GB (min)
  • Hard Disk :128 GB
  • Key Board : Standard Windows Keyboard
  • Mouse: Two or Three Button Mouse
  • Monitor: Any

S/W SPECIFICATIONS:

  • Operating System: Windows 10
  • Server-side Script : Python 3.6
  • IDE: PyCharm
  • Libraries Used : Pandas, NumPy, fbprophet,pmdarima.

Learning Outcomes

·         About Classification in machine learning.

·         About preprocessing techniques.

·         About Random Forest Classifier.

·         About Decision Tree Classifier.

·         Knowledge on PyCharm Editor.

 

 

 

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