Intelligent Internet of Things based Flood Management System

Flood control is a difficult task for many countries since rainfall patterns are changing due to global warming. As a result, a system that maximizes the benefits of secure intelligent flood management is required. In this paper, secure intelligent IoT-based flood management system is proposed using machine learning classifiers. This investigation has been performed with different machine learning classifiers for estimating flood danger in an area with insufficient data. In comparison to the existing classifiers, Gaussian Naive Bayes, Support Vector Machines, KNN and proposed system are proved to be best in estimating the flood danger. As a result, the proposed system can be trusted when it comes to estimating the danger of flooding in a certain area. The decision tree classifier is overfitted or not cannot be anticipated because the available data isn't adequate to make predictions. Moreover, an accuracy of 98.5 % is achieved, and hence the results are highly relevant and accurate, especially considering that the dataset only comprises of 172 items.