Rainfall is one of the primary triggers for many geological and hydrological natural disasters. While the geological events are related to mass movements in land collapse due to waterlogging, the hydrological ones are usually assigned to runoff or flooding. Studies in the literature propose predicting mass movement events as a function of accumulated rainfall levels recorded at distinct periods. According to these approaches, a two-dimensional rainfall levels feature space is segmented into the occurrence and non-occurrence decision regions by an empirical critical curve (CC). Although this scheme may easily be extended to other purposes and applications, studies in the literature need to discuss its use for flooding prediction. In light of this motivation, the present study is unfolded in (1) verifying that defining CCs in the rainfall levels feature space is a practical approach for flooding prediction and (2) analyzing how geospatial components interact with rainfall levels and flooding prediction. A database containing the rainfall levels recorded for flooding and non-flooding events in São Paulo city, Brazil, regarding the period 2015–2016, was considered in this study. The results indicate good accuracy for flooding prediction using only partial rain, which can be improved by adding physical characteristics of the flooding locations, demonstrating a direct correlation with spatial interactions, and rainfall levels.

中文翻译：

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分析圣保罗降雨量与洪水预测之间的空间相互作用

降雨是许多地质和水文自然灾害的主要诱因之一。虽然地质事件与内涝导致的土地塌陷的大规模运动有关，但水文事件通常与径流或洪水有关。文献研究提出，根据不同时期记录的累积降雨量来预测大规模运动事件。根据这些方法，二维降雨水平特征空间通过经验临界曲线（CC）被分割为发生和非发生决策区域。尽管该方案可以很容易地扩展到其他目的和应用，但文献研究需要讨论其在洪水预测中的用途。鉴于这一动机，本研究的展开包括（1）验证在降雨水平特征空间中定义 CC 是洪水预测的实用方法，以及（2）分析地理空间成分如何与降雨水平和洪水预测相互作用。本研究考虑了一个数据库，其中包含巴西圣保罗市 2015 年至 2016 年期间洪水和非洪水事件记录的降雨量。结果表明，仅使用部分降雨的洪水预测具有良好的准确性，可以通过添加洪水位置的物理特征来提高洪水预测的准确性，证明与空间相互作用和降雨水平的直接相关性。