The spatiotemporal interpolation model is necessary for generating continuous distributions for spatiotemporally discrete sampling points. However, there remain challenges in spatiotemporal interpolation due to the complex spatiotemporal effect and the imprecise kernel functions. Here, we proposed a spatiotemporal autoregressive neural network interpolation model (STARNN) that incorporates adaptive spatiotemporal distance quantification and supervised learning. The 10-fold cross-validation modelling on sea surface temperature and coastal nutrients demonstrated that the STARNN model performs better than baseline models and can well depict reasonable spatiotemporal distributions for environmental factors. By proposing two stacked neural networks, the STARNN model can accurately integrate spatial and temporal distances and avoids subjective selection of the kernel function. This study developed a novel interpolation model for processing discrete spatiotemporal points by following the data-driven paradigm, which can offer decision support for simulating the spread of sea temperature anomalies and optimizing the distribution of water quality measurement stations.

中文翻译：

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一种面向离散环境因子的时空自回归神经网络插值方法


时空插值模型对于为时空离散采样点生成连续分布是必需的。然而，由于时空效应复杂且核函数不精确，时空插值仍存在挑战。在这里，我们提出了一个时空自回归神经网络插值模型 （STARNN），该模型结合了自适应时空距离量化和监督学习。对海面温度和沿海营养物的 10 倍交叉验证模型表明，STARNN 模型的性能优于基线模型，并且可以很好地描述环境因素的合理时空分布。通过提出两个堆叠神经网络，STARNN 模型可以准确地整合空间和时间距离，避免了核函数的主观选择。本研究遵循数据驱动范式开发了一种新的处理离散时空点的插值模型，可为模拟海水温度异常的传播和优化水质测量站的分布提供决策支持。