Analysis of Artificial Intelligence Machine Learning Technology for Mapping and Predicting Flood Locations in Pahlawan Batu Bara Village

Abstract

This research proposes the application of artificial intelligence technology, especially machine learning, to improve flood predictions. Flooding is a serious threat that can cause major losses to society and the environment. In an effort to overcome this problem, machine learning methods are used to analyze historical data related to weather, rainfall, topography, drainage systems and other factors that influence the occurrence of floods. Machine learning algorithms such as neural networks, decision trees, and other models to predict the potential for flooding in an area. Data collected from weather sensors, satellite maps and other data sources is used to train the model so that it is able to identify patterns that lead to flooding conditions. The research results show that the machine learning approach is able to increase the accuracy of flood predictions with a better level of reliability compared to traditional methods. The implementation of artificial intelligence technology in flood prediction has great potential to provide early warning to the public and authorities, thereby reducing the negative impacts caused by flood disasters. It is hoped that this research can become the basis for developing a more effective early warning system in dealing with the threat of flooding in the future.