This study presents a comprehensive investigation into developing a real-time monitoring framework for a localized landslide early warning system (LEWS), focusing on the hydrological dynamics of an unsaturated slope near the Joshimath Badrinath Highway in Uttarakhand, India. Considering the steepness of the slope and its adjacency to the highway, continuous monitoring and stability assessment are crucial. The framework encompasses primary phases of monitoring, modelling, forecasting, and warning. Monitoring collected hydrological and meteorological data, used for modelling and calibration. Validation using Taylor diagrams ensured accuracy by comparing predicted and monitored data. The calibrated hydrological model guided slope stability modelling to identify instability factors. A machine learning algorithm detected potential instability. Forecasting predicted unstable periods, triggering warnings if the factor of safety () values drop below 1.5. The study provided important insights into slope stability, highlighting the significance of vegetation parameters in accurately assessing slope stability using the . Calibration of the hydrogeological model, particularly considering rainfall, climate, and vegetation data, improved the alignment between predicted and observed volumetric water content (VWC), especially in shallower depths. However, modelling hydrological dynamics at greater depths remains challenging, emphasizing the need for refined approaches. Machine learning techniques, specifically a Random Forest model, achieve high accuracy in predicting , identifying VWC at 0.3-m and 3-m depths as pivotal variables. Temporal evaluation suggests a 12-month simulation as optimal, showing consistent performance across depths. Overall, the study advances slope stability modelling and offers insights into sustainable slope management practices, highlighting the complex interplay between climate, vegetation, and hydrological dynamics in unsaturated slopes. This approach lays a foundation for effective LEWS in Indian Himalayan states.

中文翻译：

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集成实时传感器数据以改进喜马拉雅山西部滑坡预警中的水文土工建模


本研究对开发局部滑坡预警系统 (LEWS) 实时监测框架进行了全面调查，重点关注印度北阿坎德邦 Joshimath Badrinath 高速公路附近非饱和斜坡的水文动态。考虑到边坡的陡度及其邻近高速公路，持续监测和稳定性评估至关重要。该框架包括监测、建模、预测和预警的主要阶段。监测收集的水文和气象数据，用于建模和校准。使用泰勒图进行验证，通过比较预测数据和监测数据来确保准确性。校准的水文模型指导边坡稳定性建模以识别不稳定因素。机器学习算法检测到潜在的不稳定性。预测不稳定时期，如果安全系数 () 值降至 1.5 以下，则会触发警告。该研究为边坡稳定性提供了重要见解，强调了植被参数在使用 准确评估边坡稳定性方面的重要性。水文地质模型的校准，特别是考虑降雨、气候和植被数据，改善了预测和观测的体积含水量 (VWC) 之间的一致性，特别是在较浅的深度。然而，对更深处的水文动力学进行建模仍然具有挑战性，强调需要改进的方法。机器学习技术，特别是随机森林模型，可以实现高精度预测，并将 0.3 米和 3 米深度的 VWC 识别为关键变量。时间评估表明 12 个月的模拟是最佳的，显示出跨深度的一致性能。 总体而言，该研究推进了边坡稳定性建模，并提供了对可持续边坡管理实践的见解，强调了非饱和边坡中气候、植被和水文动态之间复杂的相互作用。这种方法为印度喜马拉雅各州有效的 LEWS 奠定了基础。