Comparative Study Of Machine Learning Algorithms for Flood Detection
DOI:
https://doi.org/10.5281/zenodo.6369640Keywords:
Machine Learning, Flood detection, Decision tree algorithm, Logistic Regression, Random Forest, Ensemble Learning, KNNAbstract
Flood is the hike of water level of water streams
and bodies. The rise of water is temporary and may last for
hours or in extreme cases last for weeks. This is caused by heavy
rain coastal rains and situations like opening of dams etc. Flood
can happen quickly slowly or quickly or can happen without any
warnings. Flood kills and make more damage than other natural
disasters. Floods are so powerful that a height of one foot can
pound a person. The escape and recovery become difficult by
the risen water. Water makes great damage and difficulties in
transportation facilities which arduous the backing of personals
and commodities. Damages in transport facilities may cause
isolation of areas. The response time is very crucial in managing
and recovery. The prediction of floods can massively decrease
the damage. This paper discusses about the prediction of floods
using different machine learning algorithms and developing
efficacious and operative Computer Application that alerts the
people which affects the flood.