Abstract: Flooding is one of the most devastating natural events that can occur. The ability to forecast this occurrence has a significant impact on the well-being of humans and other natural beings. According to a brief history of weather study, the ancient Mayans were able to predict floods using planetary motions that were not very accurate. With the advancement of technology and the increasing reliance on computers, people are now able to collect vast amounts of data of various types, such as planetary positions using mathematical models, weather data using rain gauges, and wind turbines. It is very difficult to analyse this data and provide an output, but with the help of a machine learning algorithm, one can gain higher accuracy to forecast floods and inform the region ahead of time, avoiding priceless losses.
Keywords: Gaussian Naïve Bayes, K- Nearest Neighbours, Support Vector machine, Logistic Regression, Decision Tree Classification.
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DOI:
10.17148/IJARCCE.2022.116132
