Intensifying climate change will lead to more extreme weather events, including heavy rainfall and drought. Accurate stream flow prediction models which are adaptable and robust to new circumstances in a changing climate will be an important source of information for decisions on climate adaptation efforts, especially regarding mitigation of the risks of and damages associated with flooding. In this work we propose a machine learning-based approach for predicting water flow intensities in inland watercourses based on the physical characteristics of the catchment areas, obtained from geospatial data (including elevation and soil maps, as well as satellite imagery), in addition to temporal information about past rainfall quantities and temperature variations. We target the one-day-ahead regime, where a fully convolutional neural network model receives spatio-temporal inputs and predicts the water flow 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 dense water flow intensity prediction; earlier works have considered predicting flow intensities at a sparse set of locations at a time. An extensive set of model evaluations and ablations are performed, which empirically justify our various design choices. Code and preprocessed data have been made publicly available at https://github.com/aleksispi/fcn-water-flow.

翻译：日益加剧的气候变化将导致更多极端天气事件，包括强降雨和干旱。在气候变化背景下，对新型环境条件具有适应性和鲁棒性的精确水流预测模型，将成为气候适应决策的重要信息来源，特别是在降低洪水灾害风险及损失方面。本研究提出一种基于机器学习的方法，依据集水区的物理特征（包括从地理空间数据——高程图、土壤图及卫星影像中获取的信息）以及历史降雨量和温度变化的时序信息，预测内陆水系的（考虑）水流强度。我们聚焦于次日预测模式：采用全卷积神经网络模型接收时空输入，预测空间输入中每个坐标点的次日水流强度。据我们所知，这是首次探索密集水流强度预测任务——此前研究仅考虑在稀疏位置集合上预测水流强度。通过大量模型评估与消融实验，我们实证验证了各项设计选择的合理性。代码与预处理数据已公开发布于 https://github.com/aleksispi/fcn-water-flow。