AI Based Model for Prediction of Heavy/High Impact Rain Events
- Version
- Download 76
- File Size 1.14 MB
- File Count 1
- Create Date 22 May 2024
- Last Updated 22 May 2024
AI Based Model for Prediction of Heavy/High Impact Rain Events
Aviral Srivastava1, Aruneema Joshi2, Brahmansh Dwivedi3, Neetu Kumari Rajput4
1Dept. of Information Technology, Noida Institute of Engineering and Technology. Greater Noida, Uttar Pradesh, 0201ite040@niet.co.in
2Dept. of Information Technology, Noida Institute of Engineering and Technology. Greater Noida, Uttar Pradesh, 0201ite129@niet.co.in
3Dept. of Information Technology, Noida Institute of Engineering and Technology. Greater Noida, Uttar Pradesh, 0201ite041@niet.co.in
4 Assistant Professor Dept. of Information Technology, Noida Institute of Engineering and Technology. Greater Noida, Uttar Pradesh, neetu.rajput@niet.co.in
Abstract - India is a farming country whose economy is heavily reliant on the development of rainforests. In order to analyse agricultural yields, rainfall estimates are essential and fundamental for all ranchers. The ability to predict the climate with the aid of science and innovation is unsurprising rainfall. To effectively use water resources, horticulture production, and water arranging, it is important to determine the amount of rainfall. Rainfall can be predicted using a variety of information mining techniques. The process of information extraction is used to evaluate rainfall. Probably the most well-known rainfall forecast calculations may be found in this article. Some of the computations compared with this record include Logistic Regression, K-Neighbors Classifier, Random Forest Classifier, and Certificate Tree. It is possible to decompose the process of precisely predicting rainfall from a relative standpoint. To do this, we gathered satellite data. We have taken a number of variables into account in order to predict (temperature, dew point, humidity, wind pressure, wind speed, wind direction, etc.).
Key Words: Random Forest Classifier, KNeighbors Classifier, Logistic Regression, Oversampling, Under sampling, and Rainfall Prediction.
Download