A combined method was developed to forecast the spatial and the temporal probability of occurrence of rainfall-induced shallow landslides over large areas. The method also allowed to estimate the dynamic change of this probability during a rainfall event. The model, developed through a data-driven approach basing on Multivariate Adaptive Regression Splines technique, was based on a joint probability between the spatial probability of occurrence (susceptibility) and the temporal one. The former was estimated on the basis of geological, geomorphological, and hydrological predictors. The latter was assessed considering short-term cumulative rainfall, antecedent rainfall, soil hydrological conditions, expressed as soil saturation degree, and bedrock geology. The predictive capability of the methodology was tested for past triggering events of shallow landslides occurred in representative catchments of Oltrepò Pavese, in northern Italian Apennines. The method provided excellently to outstanding performance for both the really unstable hillslopes (area under ROC curve until 0.92, true positives until 98.8%, true negatives higher than 80%) and the identification of the triggering time (area under ROC curve of 0.98, true positives of 96.2%, true negatives of 94.6%). The developed methodology allowed us to obtain feasible results using satellite-based rainfall products and data acquired by field rain gauges. Advantages and weak points of the method, in comparison also with traditional approaches for the forecast of shallow landslides, were also provided.

中文翻译：

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开发流域尺度上时空浅层滑坡发生概率的数据驱动模型

开发了一种联合方法来预测大面积降雨引起的浅层滑坡发生的空间和时间概率。该方法还允许估计降雨事件期间该概率的动态变化。该模型通过基于多元自适应回归样条技术的数据驱动方法开发，基于空间发生概率（敏感性）和时间概率之间的联合概率。前者是根据地质、地貌和水文预测指标进行估计的。后者的评估考虑了短期累积降雨量、前期降雨量、土壤水文条件，表示为土壤饱和度和基岩地质。该方法的预测能力已针对在意大利北部亚平宁山脉的 Oltrepò Pavese 代表性集水区发生的浅层滑坡的过去触发事件进行了测试。该方法对真正不稳定的山坡（ROC 曲线下面积为 0.92，真阳性为 98.8%，真阴性高于 80%）和触发时间的识别（ROC 曲线下面积为 0.98，真96.2% 的阳性，94.6% 的真阴性）。开发的方法使我们能够使用基于卫星的降雨产品和现场雨量计获取的数据获得可行的结果。还提供了与传统的浅层滑坡预测方法相比，该方法的优点和缺点。