Effective water resource management requires information on water availability, both in terms of quality and quantity, spatially and temporally. In this paper, we study the methodology behind Transfer Learning (TL) through fine-tuning and parameter transferring for better generalization performance of streamflow prediction in data-sparse regions. We propose a standard recurrent neural network in the form of Long Short-Term Memory (LSTM) to fit on a sufficiently large source domain dataset and repurpose the learned weights to a significantly smaller, yet similar target domain datasets. We present a methodology to implement transfer learning approaches for spatiotemporal applications by separating the spatial and temporal components of the model and training the model to generalize based on categorical datasets representing spatial variability. The framework is developed on a rich benchmark dataset from the US and evaluated on a smaller dataset collected by The Nature Conservancy in Kenya. The LSTM model exhibits generalization performance through our TL technique. Results from this current experiment demonstrate the effective predictive skill of forecasting streamflow responses when knowledge transferring and static descriptors are used to improve hydrologic model generalization in data-sparse regions.
翻译:有效的水资源管理要求从质量和数量、空间和时间角度提供关于水的可得性的信息。在本文件中,我们通过微调和参数转移研究转移学习(TL)背后的方法,以便在数据偏少的区域更好地普及流流预测;我们提议以长期短期内存(LSTM)为形式的标准经常性神经网络,以适应足够大的来源域数据集,并将所学到的权重重新定位为相当小但相近的目标域数据集。我们提出一种方法,通过将模型的空间和时间部分分开,对模型进行空间应用应用应用的转移学习方法,并培训模型,以便根据代表空间变异性的绝对数据集进行概括化。这个框架是根据美国丰富的基准数据集制定的,并以肯尼亚自然保护公司所收集的较小数据集进行评估。LSTM模型通过我们的TL技术展示了一般化的绩效。这一试验的结果表明,在知识转移和静态描述器用于改进数据偏小区域的水文模型概括化时,预测流流流反应的有效预测技能。