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:加剧的气候变化将导致更多极端天气事件,包括暴雨和干旱。准确的流量预测模型是应对气候变化中新情况的一种重要信息来源,尤其涉及减轻洪水风险和相关损害的气候适应措施时。在这项工作中,我们提出了一种基于机器学习的方法,以集成水文地理空间数据(包括海拔和土壤地图以及卫星图像)和关于过去降雨量和温度变化的时间信息,来预测内陆水道的水流强度。我们目标是一天内提前预测水流强度,使用全卷积神经网络模型,在经过时间和空间处理的输入数据下,预测下一天每个坐标内的水流强度。据我们所知,我们是首批处理密集水流强度预测的研究者;以前的研究考虑了预测某一时刻稀疏位置的流量强度。我们进行了大量模型评估和抨击,从经验上证明了我们的多种设计选择的合理性。代码和预处理数据已公开在 https://github.com/aleksispi/fcn-water-flow 上提供。