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Intensifying climate change will lead to more extreme weather events,including heavy rainfall and drought. Accurate stream flow prediction modelswhich are adaptable and robust to new circumstances in a changing climate willbe an important source of information for decisions on climate adaptationefforts, especially regarding mitigation of the risks of and damages associatedwith flooding. In this work we propose a machine learning-based approach forpredicting water flow intensities in inland watercourses based on the physicalcharacteristics of the catchment areas, obtained from geospatial data(including elevation and soil maps, as well as satellite imagery), in additionto temporal information about past rainfall quantities and temperaturevariations. We target the one-day-ahead regime, where a fully convolutionalneural network model receives spatio-temporal inputs and predicts the waterflow intensity in every coordinate of the spatial input for the subsequent day.To the best of our knowledge, we are the first to tackle the task of densewater flow intensity prediction; earlier works have considered predicting flowintensities at a sparse set of locations at a time. An extensive set of modelevaluations and ablations are performed, which empirically justify our variousdesign choices. Code and preprocessed data have been made publicly available athttps://github.com/aleksispi/fcn-water-flow.

Fully Convolutional Networks for Dense Water Flow Intensity Prediction in Swedish Catchment Areas