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Landslides are phenomena widely present around the world and responsible each year of numerous life loss and extensive property damage. Researchers have developed various methodologies to identify area of high susceptibility of landslides. However, these methodologies cannot predict 'when' landslides are going to take place. Indeed, Wireless Sensors Network (WSN), Internet of Things (IoT) and Artificial Intelligence (AI) offer the possibility to monitor in real-time parameters causing the triggering factors of rapid landslides. In this paper, we suggest a real-time monitoring of landslides in order to precociously alert population in dangerous situation by means of a warning system. The novelty of this paper is the coupling of wireless sensors network and a multi-agent system deployed on an edge AI-IoT architecture by means of Kubernetes and Docker. [ABSTRACT FROM AUTHOR]

Abstract: In this paper all the phases for the realization of the early warning system for the rockslide of Torgiovannetto in Central Italy are described. The landslide consists in a 182,000m3 rock wedge threatening two roads which are important for local transportation. The present work encompasses all the components of an early warning system, including the geological knowledge, the risk scenarios, the kinematic characterization of the landslide, the choice and installation of the monitoring system, the setting of appropriate alarm levels and the definition of plans of civil protection. The focus is on practical and logistical issues met in all these phases and the counter-measures adopted. At present the system consists in 13 wire extensometers, 1 thermometer, 1 rain gauge and 3 cameras. Should a velocity threshold be exceeded by two or more sensors, the attention level would be entered, causing improved monitoring and surveillance. In case the behavior of the landslide changes and, by using expert judgment and forecasting methods, an imminent failure is hinted, then an alarm is issued and the upper road is closed. This paper can provide ideas and solutions for a landslide early warning system that aims to be simple, flexible, versatile and with a low probability of giving false alarms. [Copyright &y& Elsevier]

The upper Minjiang catchment is frequently affected by a significant number of large to giant landslides. In one such massive older landslide, approximately 2 million m3 of sediment was reactivated, threatening the safety of a pylon on the landslide and the safety of the only route that allows high-speed travel from Wenchuan to Maoxian. To trace the deformation evolution history and analyze the current deformation of the new landslide, we examined SAR data since 2007, carried out geological and UAV surveys, and designed and installed real-time monitoring equipment. This paper presents the main spatial and temporal characteristics of the landslide. We found that the landslide has experienced creep deformation in the past five years and its sensitivity to rainfall. The development process of the landslide from the formation of tensile crack at the trailing edge to the gradual pushing forward of the crack propagation was revealed. The innovation of this paper is to reveal the whole process of the spatial-temporal evolution from its beginning to large deformation of a real case of creep landslide, which can provide a very good reference for the analysis and monitoring of such creeping landslide evolution process. This representative case also shows how to combine remote sensing and in situ monitoring methods to carry out continuous monitoring and the establishment of a warning system for potential landslide sites, so as to trace the evolution history, derive activity maps and analyze development trend, which can significantly reduce landslide risk. Additional mitigation procedures can be put into place if continuous monitoring of the Minjiang River Canyon occurs, resulting in a reduction of direct losses. In particular, modeling of the slope deformation and failure, damming process, and prospective flooding can be done in advance. Therefore, it is expected that this report can strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods and in situ monitoring systems as an approach to reduce risk. • The whole process of the spatial-temporal evolution of a real case of creep landslide is revealed. • This representative case shows how to combine remote sensing and in situ monitoring methods to carry out continuous monitoring. • Ground-based real-time monitoring provides accurate landslide early warning. [ABSTRACT FROM AUTHOR]

Central Italy is characterized by complex orography. The territorial response to heavy precipitation may activate different processes in terms of hydrogeological hazards. Floods, flash floods, and wet mass movements are the main ground effects triggered by heavy or persistent rainfall. The main aim of this work is to present a unique tool that is based on a distributed hydrological model, able to predict different rainfall-induced phenomena, and essential for the civil protection early warning activity. The Cetemps Hydrological Model is applied to the detection of hydrologically stressed areas over a spatial domain covering the central part of Italy during a weather event that occurred in 2014. The validation of three hydrological stress indices is proposed over a geographical area of approximately 64,500 km2 that includes catchments of varying size and physiography. The indices were used to identify areas subject to floods, flash floods, or landslides. Main results showed very high accuracies (~90%) for all proposed indices, with flood false alarms growing downstream to larger basins, but very close to zero in most cases. The three indices can give complementary information about the predominant phenomenon and are able to distinguish fluvial floods from pluvial floods. Nevertheless, the results were influenced by the presence of artificial reservoirs that regulated flood wave propagation, therefore, indices timing slightly worsen downstream in larger basins. [ABSTRACT FROM AUTHOR]

Identification of the influence of hydrological and geological factors on slope deformation characteristics (i.e., the displacement–time relationship) under rainfall plays a crucial role in providing early warning information and enabling the implementation of emergency remedial actions before a landslide occurs. In this study, a series of coupled hydro-mechanical finite element analyses were performed to investigate slope displacement behavior triggered by rainfall infiltration. First, the numerical model was validated by comparing the predicted displacement with those measured from a full-scale landslide flume test. A parametric study was then conducted, considering various hydrological conditions and soil hydraulic and mechanical parameters that were statistically determined from a large soil database compiled from relevant literature. Further, the influences of the aforementioned factors on the timing, magnitude, and rate of slope displacement prior to landslide occurrence were quantitatively evaluated in a sensitivity assessment. The numerical results indicated that the slope deformation characteristics could be significantly influenced by various hydrological and geological factors. Nevertheless, the slope displacement over time for all cases generally can be divided into three stages, namely the constant, accelerated, and critical deformation stages, which correspond to various states of slope movement and pore water pressure development. The relationships of slope displacement magnitude and displacement rate with the factor of safety were established, which provide a valuable information in practice for engineers to interpret the slope stability level from a large quantity monitoring data of slope displacement. [ABSTRACT FROM AUTHOR]

A regional-scale landslide early warning system was developed in collaboration with a city government. The structure and distinctive features of the system are described in detail. This system employs the principles of the sequential evaluation method that consecutively applies three different evaluation stages: statistical, physically based, and geomorphological evaluations. Based on this method, the system determines five phases of warning levels with improved levels of certainty and credibility. In particular, the warning levels are systematically derived to enable the discrimination of slope failures and debris flows. To provide intuitive and pragmatic information regarding the warning capabilities of the system, a comprehensive performance analysis was conducted. Early warning level maps were generated and a historical landslide database was established for the study period from 2009 to 2016. As a result, 81% of historical slope failures and 86% of historical debris flows were correctly predicted by high-class warning levels. Miscellaneous details associated to the timing efficiency of warnings were also investigated. Most notably, five high-class warning level events and four landslide events were recorded for a study region during the eight-year period. The four landslide events were all successfully captured by four out of the five warning events. [ABSTRACT FROM AUTHOR]

Understanding the hydrological mechanism of rainfall-induced shallow landslides is critical to make reliable predictive assessments and to reduce risks of economic loss and fatalities. An integrated approach was identified by combining the field-based monitored hydrological data with a hydro-mechanical analysis, which can be utilized to better understand seasonal hydrological features in shallow soils and the corresponding triggering mechanism of rainfall induced shallow landslides, with the aim of setting up an improved landslide early warning system. A hydrological monitoring system on a representative slope that was experiencing frequent local shallow failures in the Ren River catchment was constructed to provide available seasonal hydrological features with regard to time trends in volumetric water content and pore water pressure in shallow soils. A transition of vertical downward flow from unsaturated to near-saturated conditions has been fully detected since the significant saturation degree and associated pore pressure varies with vertical depths. Consequently, the triggering mechanism of shallow landslides should be associated with the combined effects of dissipation of matrix suction in the upper unsaturated zone and increased positive pore pressure due to the generation of a temporal perched water table in the deeper saturated zone. This process was confirmed through the field evidence and a back analysis on the long-term hydrological evolution of slope soil using a calibrated one-dimensional hydrological model. Antecedent hydrological conditions were also found to have an effect on rainfall intensity–duration thresholds in the landslide early warning system based on a hydro-mechanical analysis, with the aim of improving the accuracy of the model's predictive ability. The results indicated that this research promoted the effectiveness of a landslide early warning system through a hydro-mechanical analysis based on capturing the real hydrological features of a landslide. [ABSTRACT FROM AUTHOR]