Shallow landslides are among the natural threats causing death and damage. They are mostly triggered by rainfall in mountainous areas where precipitation used to be abundant. The amount of rainfall inducing this natural threat differs from one site to another based on the geographical characteristics of that area. In addition to the rainfall depth, the determination of soil water content in a specific zone has a major contribution to the landslide prediction and early warning systems. Rwanda being a country with hilly terrains, some areas are susceptible to both rainfall and soil water content inducing landslides. But an analytical study of the physical threshold determination of both rainfall and soil water content inducing landslides is lacking. Therefore, this experimental study is conducted to determine the rainfall and soil water content threshold that can be fed in to the landslide early warning system (LEWS) for alert messages using the Internet of Things (IoT) technology. Various experiments have been conducted for the real-time monitoring of slope failure using the toolset composed of a rain gauge, soil moisture sensors, and a rainfall simulating tool. The results obtained show that the threshold for landslide occurrence does not solely correlate with the total rainfall amount (or intensity) or soil moisture, but also influenced by internal (geological, morphological) and environmental factors. Among the sampled sites, the sites covered by forest indicated no sign of slope failure, whereas sites with crops could slip. The experiments revealed that for a specific site, the minimum duration to induce slope failure was 8 hours, 41 minutes with the rainfall intensity of 8 mm/hour, and the soil moisture was above 90% for deeper sensors. These values are used as thresholds for LEWS for that specific site to improve predictions.

中文翻译：

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特定地点土壤含水量和降雨诱发浅层滑坡的实验研究：以卢旺达加肯克区为例

浅层滑坡是造成死亡和破坏的自然威胁之一。它们主要是由过去降水丰富的山区的降雨引发的。导致这种自然威胁的降雨量因该地区的地理特征而异。除了降雨深度之外，特定区域土壤含水量的确定对滑坡预测和预警系统也有重大贡献。卢旺达是一个丘陵地带的国家，一些地区容易受到降雨和土壤含水量的影响，从而引发山体滑坡。但缺乏对降雨和土壤含水量诱发滑坡的物理阈值确定的分析研究。所以，本实验研究的目的是确定降雨量和土壤含水量阈值，这些阈值可以使用物联网 (IoT) 技术输入到滑坡预警系统 (LEWS) 以获取警报消息。已经使用由雨量计、土壤湿度传感器和降雨模拟工具组成的工具集对边坡失稳进行实时监测进行了各种实验。结果表明，滑坡发生的阈值不仅与总降雨量（或强度）或土壤水分有关，还受内部（地质、形态）和环境因素的影响。在采样点中，被森林覆盖的点没有坡度崩塌的迹象，而有庄稼的点可能会滑落。实验表明，对于特定的站点，在降雨强度为 8 毫米/小时的情况下，诱发边坡破坏的最短持续时间为 8 小时 41 分钟，并且土壤湿度在 90% 以上对于更深的传感器。这些值用作该特定站点的 LEWS 阈值以改进预测。