Abstract
The effects of climate change create climatic temporal imbalances that favor the development of hydrometeorological phenomena and cause socioeconomic damage when they occur. In the absence of Early Warning Systems and dedicated monitoring stations, the effectiveness of a Convolutional Neural Network model is tested to interpret and label dataset on the climatic conditions of the Misantla’s river basin and its surroundings, regarding to the flood hazard level. Aiming to classify rainfall events in the region using dataset collected through 3 weather stations around the region: northern zone of Veracruz, Mexico, specifically the municipality of Misantla. Neural networks can maximize the use of dataset collected by weather stations, providing a safer environment in the event of floods, and having a positive effect on the preservation of human activity. The dataset provided allows to label data as ‘GREEN’, ‘YELLOW’ and ‘RED’ with more than 95% accuracy, performing better when working with a large number of validation data, but also shows a slowdown during the integration of larger training data sets.
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García-Trujillo, OA., Herazo, L.C.S., Sánchez-DelaCruz, E., Moreno, R.G. (2022). Classification of Flood Warnings Applying a Convolutional Neural Network. In: Pichardo Lagunas, O., Martínez-Miranda, J., Martínez Seis, B. (eds) Advances in Computational Intelligence. MICAI 2022. Lecture Notes in Computer Science(), vol 13612. Springer, Cham. https://doi.org/10.1007/978-3-031-19493-1_16
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