Flood Disaster Detection Based on Rainfall using Random Forest Algorithm
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Abstract
This research was conducted to detect worldwide natural disasters, namely floods based on rainfall, using the Random Forest algorithm. This research can also help the government estimate what will be done in the future when a flood disaster occurs. The method is to collect rainfall and flood disaster data from Kaggle. The data is then cleaned, processed, and tested for reliability. Using a suitable dataset for testing and choosing the correct algorithm can ensure that the data mining process produces accurate information. Furthermore, the Random Forest algorithm was applied to the data to classify flood disasters based on rainfall. The results showed that the Random Forest algorithm could provide flood disaster classification results with a high accuracy rate of 95.8%. The novelty of this research lies in using the Random Forest algorithm, which is rarely used in flood disaster classification research based on rainfall. This research is expected to contribute to developing a more accurate and reliable rainfall-based flood disaster early warning system.
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