Advanced flood prediction at forest with rainfall data using various machine learning algorithms
The aim is to classify and predict floods in advance with rain data patterns of India using spatio-temporal logic. Two Classification algorithms are used to achieve the maximum accuracy namely K-Nearest Neighbour with a sample size=5 and Logistic Regression with a sample size=5 for continues iterat...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2024
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/28725/1/Advanced%20Flood%20Prediction%20at%20Forest%20with%20Rainfall%20Data%20Using%20Various%20Machine%20Learning%20Algorithms.pdf http://eprints.utem.edu.my/id/eprint/28725/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The aim is to classify and predict floods in
advance with rain data patterns of India using spatio-temporal logic. Two Classification algorithms are used to achieve the maximum accuracy namely K-Nearest Neighbour with a sample size=5 and Logistic Regression with a sample size=5 for continues iterations. The work focused towards comparison of K-Nearest Neighbour and logistic regression, which has confidential and forecast the standards from the rainfall statistics to produce estimated accuracy with K-nearest neighbour has higher accuracy by comparing with Logistic Regression accuracy. It has a high accuracy of 50.35%, in comparison with the Logistic Regression algorithm 45.96%.
The significant values have been statistically defined with the
value of (p< 0.001). Prediction in flood patterns, K-Nearest
Neighbour consisting rainfall pattern expressively used to
produce improved accuracy than the Logistic Regression. |
|---|