Machine Learning applied to hidrology
Every year million of people are involved in floods resulting in property damage and loss of human lives. Accurate prediction is therefore essential to be able to take countermeasures. In recent years traditional analytical methods for hydrological quantities forecasting has been outclassed by machine learning techniques. This report investigates the performance of four machine learning models: Feed Forward, Long-short memory, Temporal convolutional and Attention models, on their ability to learn the relation between rainfall and subsequent river level surge.
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