Development of the new methods of surface water observation is crucial in the perspective of increasingly frequent extreme hydrological events related to global warming and increasing demand for water. Orthophotos and digital surface models (DSMs) obtained using UAV photogrammetry can be used to determine the Water Surface Elevation (WSE) of a river. However, this task is difficult due to disturbances of the water surface on DSMs caused by limitations of photogrammetric algorithms. In this study, machine learning was used to extract a WSE value from disturbed photogrammetric data. A brand new dataset has been prepared specifically for this purpose by hydrology and photogrammetry experts. The new method is an important step toward automating water surface level measurements with high spatial and temporal resolution. Such data can be used to validate and calibrate of hydrological, hydraulic and hydrodynamic models making hydrological forecasts more accurate, in particular predicting extreme and dangerous events such as floods or droughts. For our knowledge this is the first approach in which dataset was created for this purpose and deep learning models were used for this task. Additionally, neuroevolution algorithm was set to explore different architectures to find local optimal models and non-gradient search was performed to fine-tune the model parameters. The achieved results have better accuracy compared to manual methods of determining WSE from photogrammetric DSMs.
翻译:从与全球变暖有关的极端水文事件日益频繁和对水的需求日益增加的角度来看,开发新的地表水观测方法至关重要,因为与全球变暖有关的极端水文事件日益频繁,对水的需求日益增加。使用UAV摄影测量法获得的奥多光谱和数字地表模型(DSM)可用于确定河流的水面升幅(WSE),然而,由于光测量算算法的限制,DMS的水面受到干扰,这项任务很困难。在本研究中,机器学习被用来从扰乱的光度测算数据中提取WSE值。水文和水文动力学模型(DSM)已经为此专门设计了一个品牌新数据集。新方法是向高空间和时间分辨率水表水平测量自动化测量方法迈出的重要一步。这些数据可以用来验证和校准水文、水文和水力学模型(WSE),使水文预报更加精确,特别是预测洪水或干旱等极端和危险事件。据我们所知,这是为这一目的创建数据集的第一个方法,为此使用了深层次的学习模型。此外,神经进变算法是探索不同模型的重要一步,以便比较地确定最佳的SE-SE-SE-rographram模型,以便比较地算得出了比得得得得的模型。