Floods, being among nature\u27s most significant and recurring phenomena, profoundly impact the lives and properties of tens of millions of people worldwide. As a result of such events, social media structures like Twitter often emerge as the most essential channels for real-time information sharing. However, the total volume of tweets makes it hard to manually distinguish between those relating to floods and those that are not. This poses a large obstacle for responsible government officials who need to make timely and well-knowledgeable decisions. This study attempts to overcome this challenge by utilizing advanced techniques in natural language processing to effectively sort through the extensive volume of tweets. The outcome we obtained from this process is promising, as the XLNET model achieved an extraordinary F1 rating of 0.96. This high degree of overall performance illustrates the model’s usefulness in classifying flood-related tweets. By leveraging the abilities of the XLNET model, we aim to provide a valuable guide for responsible governance, aiding in making timely and well-informed choices during flood situations. This, in turn, will assist reduce the impact of floods on the lives and property-affected communities around the world
