A Prediction Model for Minimization of Flood Effects using Machine Learning Algorithms
N. Uttam Reddy, P. Venkatesh Kumar, Nellore Kapileswar, Judy Simon, P. Phani Kumar
Abstract
The most devastating hazard occurred by the disturbances of the environment are floods. These are considered as the ruinous natural disasters and their prediction is very complicated. Several researches are being conducted on advances in flood prediction models that reduce the calamity, mitigate loss of life, and reduce flood-related property damage. To reduce the complexity of physical processes in the floods, a vast predictive system, neural network techniques have made significant contributions by stipulating accurate performance and user economical solutions. This paper is depicting a clear path for the flood prediction using machine learning algorithms by Random Forest classifier and Decision Tree techniques. The observation from the report analyses that the performance of Flood prediction identifies the calamity and descripted in the confusion matrix with effective positive rates using python framework.