M.S., Saravanan and S., Sivashankar and A., Rajesh and Mat Ibrahim, Masrullizam (2024) Advanced flood prediction at forest with rainfall data using various machine learning algorithms. In: Proceedings of the 2024 3rd Edition of IEEE Delhi Section Flagship Conference, DELCON 2024, 21-23 November 2024, New Delhi, India.
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Advanced Flood Prediction at Forest with Rainfall Data Using Various Machine Learning Algorithms.pdf Restricted to Registered users only Download (620kB) |
Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Rainfall data, K-Nearest neighbour, Logistic regression, Flood prediction, Machine learning, Novel spatio temporal logic |
| Divisions: | Faculty Of Electronics And Computer Technology And Engineering |
| Depositing User: | Wizana Abd Jalil |
| Date Deposited: | 22 Apr 2025 08:43 |
| Last Modified: | 22 Apr 2025 08:43 |
| URI: | http://eprints.utem.edu.my/id/eprint/28725 |
| Statistic Details: | View Download Statistic |
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