Regional landslide early warning using rainfall characteristics

An early warning system can be an effective measure to reduce the damage caused by landslides by facilitating the timely evacuation of residents from a landslide-prone area. Early detection of landslide triggering across a broad range of natural terrain types can be accomplished by monitoring rainfall and the physical property changes of soils in real time or near-real time. This study involved the installation of a real-time monitoring system to observe physical property changes in soils in a valley during rainfall events. This monitoring included the measurement of volumetric water content, which was compared with the results of laboratory flume tests to identify landslide indicators in the soils. The response of volumetric water content to rainfall events is more immediate than that of pore-water pressure, and volumetric water content retains its maximum value for some time before slope failure. Therefore, an alternative method for landslide monitoring can be based on the observation of volumetric water content and its changes over time at shallow soil depths. Although no landslide occurred, the field monitoring results showed a directly proportional relationship between the effective cumulative rainfall and the gradient of volumetric water content per unit time (t/t max). This preliminary study thus related slope failure to the volumetric water content gradient as a function of rainfall. Laboratory results showed that a high amount of rainfall and a high gradient of volumetric water content could induce slope failure. Based on these results, it is possible to suggest a threshold value of the volumetric water content gradient demarcating the conditions for slope stability and slope failure. This threshold can thus serve as the basis of an early warning system for landslides considering both rainfall and soil properties.

The purpose of this study is to determine the relationship between tropical environmental climate and leakage current in high voltage insulators. This study describes the effect of tropical climate on outdoor insulators by considering the influence of natural tropical climate, humadity, temperature, ultraviolet rays, and rainfall in real time. leakage current data and environmental climate are obtained from monitoring for 2 years using a microcontroller. The test samples used are silicone rubber types of line post and suspension, and porcelain insulators installed in the 20kv distribution network where this test illustration can represent real conditions in the field. this leakage current test refers to the iec 60060 standard with a real-time leakage current monitoring system model and tropical climate conditions. The test results conducted when without rain show that temperature affects the leakage current flowing on the surface of the insulator because it has a significant value of 0.017 (p < 0.05), as well as uv ultra violet light also affects the leakage current because of a significant value of 0.037 (p < 0.05), but humidity partially does not affect the value of leakage current on the surface of the insulator because of its significant value of 0.721 (p>0.05), while when there is rain, the effect of rainfall on leakage current is 55.7%. so partially only rainfall

Modern natural hazard monitoring systems, utilizing various platforms and sensors, support risk management and Early Warning Systems (EWSs). A crucial aspect of hazard prediction is detecting spatial and temporal changes in landslide areas and identifying their precursors. Despite the rapid development of modern measurement techniques, such as remote sensing, accurately monitoring landslide areas remains challenging. These challenges arise from the diversity of landslide types, the nature and density of vegetation cover, and the limitations associated with the spatial resolution of the acquired data, which may affect the detection of changes in the study areas. This study presents an analysis of optical images and radar interferograms for selected landslide areas to identify precursors and characterize landslide dynamics.The analyses included a time series of changes in normalized vegetation indices and radar interferogram coherence. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), Moisture Stress Index (MSI), and Normalized Moisture Stress Index (NMSI) were examined, along with radar image coherence. Integrating these data types enhances monitoring efficiency by combining information from different measurement techniques, providing complementary insights, and enabling a better understanding of landslide dynamics.The conducted analysis of high-frequency measurement data revealed that normalized vegetation indices in many cases showed significant changes in landslide-prone areas before the landslides occurred. Decreases in coherence coefficient values over the same period also indicated significant changes in the analyzed areas, further confirming the occurrence of displacement in these areas. The observed correlation between the decrease in coherence and changes in vegetation index values suggests that landslide processes affected both the terrain structure and vegetation cover. Integrating optical and radar satellite data shows the potential for identifying landslide precursors and evaluating landslide activity. Such analyses can significantly support the development of landslide risk assessment tools and EWSs.

Guwahati city located in the northeastern state of Assam is prone to landslides. Each year, there are reports of loss of life and property due to landslides. Conventional methods of slope stability are yet to find a practical application in many of the human inhabited hill slopes of the city. Instead, the practice of cutting of slopes at steep angles to build houses is a rampant practice in Guwahati city. Hence, this study suggests the setting up of landslide early warning system (LEWS), where the people residing in landslide prone areas would be warned ahead of a fatal slope failure. To achieve this aim, this study proposes a two component LEWS consisting of a landslide susceptibility database and real-time monitoring of the site conditions. Based on this framework, the landslide prone hill slopes in Guwahati city have been identified. In situ properties of the hill slopes such as the soil type, permeability, slope angle, and elevation are proposed to be used as preparatory input parameters for the landslide susceptibility database component of the LEWS. The second component would be real-time monitoring of the changes in the pore water and slope angle of the hill slopes. Based on the preset threshold limits, an alarm would be issued to warn the residents of the site.KeywordsLandslide early warning systemRainfallEarthquakeGuwahati city

Landslide prediction and early warning system is an important application where sensor networks can be deployed to minimize loss of life and property. Due to the dense deployment of sensors in landslide prone areas, clustering is an efficient approach to reduce redundant communication from co-located sensors. In this paper we propose two distributed clustering and multi-hop routing protocols, CAMP and HBVR, for this problem. While CAMP is a new clustering and routing protocol, HBVR is an enhancement of BVR with HEED. We further enhance CAMP and HBVR with TEEN, a threshold based event driven protocol. TEEN is most suitable protocol for this application since different rock types can have different thresholds for stress values. Simulation results show that CAMP-TEEN gives the best performance with respect to network life time and energy consumption.

Automatic irrigation system with rain fall detection in agricultural field

A correlation between landslides and rainfall data along ali san highway of taiwan

Intense rainfall is the most important landslide trigger. In many mountainous environments of the world, heavy rainfall has caused many landslides and slope failures in a matter of seconds without warning. Therefore, an early warning system can be an effective measure to reduce the damage caused by landslides and slope failures by facilitating the timely evacuation of people from landslide-prone areas. In this study, we propose an idea to correlate soil moisture changes and deformations in slope surface by means of elastic wave propagation in soil. Constant shear stress drained triaxial tests where water was infiltrated from the bottom of specimen until failure, and slope model tests under artificial rainfall were performed to investigate the response of elastic wave velocities during pre-failure phases of rainwater infiltration and deformation. Analysis of the results has established that the elastic wave velocity continuously decreases in response of moisture content and deformation, and there was a distinct surge in the decrease rate of wave velocity when failure was initiated. Possible mechanisms were interpreted based on the test results. It is proposed that a warning be issued at switch of wave velocity decrease rate. This approach can thus serve as the basis of an early warning system for landslides and slope failure considering both moisture content and deformation.

Coastal areas are increasingly vulnerable to abrasion, flash floods, and seawater intrusion due to climate change and human activities. Addressing these hazards requires an integrated early warning system that enables timely and adaptive disaster mitigation. This study integrates landscape analysis, Mobile GIS technology, and the Ant algorithm into a unified early warning framework. Landscape analysis identifies geomorphological patterns and coastline changes. Mobile GIS supports real-time spatial data acquisition, while the Ant algorithm optimizes evacuation routes, sensor placement, and hazard prediction through agent-based modeling. The system incorporates spatial, topographic, oceanographic, and socio-economic data processed through spatial and computational methods. Validation using historical records and field data shows enhanced hazard detection, more efficient evacuation planning, and quicker response times. The Ant algorithm adapts routes in real time based on environmental changes. Sensor deployment is optimized for high-risk zones. Mobile GIS ensures continuous updates and spatial visualization. Real-time processing supports rapid decision-making and threat modeling. These integrated components demonstrate a strong potential to build a resilient, data-driven early warning system for coastal communities. In conclusion, the proposed system offers a precise, adaptive, and scalable approach to improve disaster preparedness in vulnerable coastal regions. Keywords: early warning system, Mobile GIS, ant algorithm, landscape analysis, coastal disaster mitigation

This work presents a comparative study between two approaches to calibrate radar rainfall in real time. The weather radar provides continuous measurements in real-time which have errors of either meteorological or instrumental nature. Locally, gauge measurements have a greater performance than radar measurements that can be used to improve radar estimates. One way of doing that is via a state space representation associated to the Kalman filter algorithm. In the single-site modeling approach we use the linear calibration model applied in [1] and [3] while the multivariate state-space model proposed in [6] is used in the multiple site approach. This work aims to discuss and compare these two different state space formulations based on the same data set.

Cloud photographs from satellites as a hydrological tool in remote equatorial regions

Leveraging virtualization technology, Mobile Edge Computing (MEC) deploys multiple services with different execution time requirements running as isolated processes. For instance, both real-time (RT) and non-RT applications may be (are) running on the same infrastructure using containerized virtualization. Nevertheless, sharing resources (e.g., CPU) with collocated workloads could impact the RT performance of RT applications. This paper presents PRINCIPIA, a dynamic CPU and CPU-shares allocation mechanism that opportunistically enables non-RT applications to run on underutilized CPUs while providing RT guarantees to RT applications. By monitoring MEC’s system metrics like processor’s CPU utilization and container’s CPU usage, PRINCIPIA dynamically allocates both CPU and CPU-shares to containers running non-RT applications aiming at opportunistically exploiting underutilized CPUs by containers running RT applications. We evaluate PRINCIPIA on a small-scale MEC server which uses containerized virtualization along with Linux RT Kernel to deploy both RT and non-RT applications. Our findings show that PRINCIPIA mitigates the impact on the RT performance of RT applications providing bounded processing latency in comparison with the default host Kernel scheduler.

Situational awareness of urban flooding during storm events is important for disaster and emergency management. However, no general purpose tools yet exist for rendering rainfall accumulations in real-time at the resolution of hydrologic units used for modeling. This demonstration will exhibit a novel web 2.0 visual analytical approach for understanding and adaptively managing urban flooding issues. The approach generates a geospatial-temporal map of rainfall within urban hydrologic units (sewer-sheds) in real-time. The polygon-averaged rainfall data is generated using virtual sensors which provide customized real-time data products derived from National Weather Service weather radar data using NCSA's workflow tools. Time-series KML (Keyhole Markup Language) layers are generated, where each KML layer represents a particular slice of the geospatial color-coded sewershed map. Such time-aware KML can be replayed as a movie in the web-based Google Earth environment. This geospatial visual analytic approach can provide decision markers and communities a powerful resource for assessment of neighborhood flooding issues. We will demonstrate our technology using historical and real-time rainfall data in the metropolitan Chicago area to show the effectiveness of such approach. Future work by combining additional ground-truth flooding data will allow us move towards real-time improved decision support for flooding and stormwater management.

The city of Matagalpa, due to the geological and morphological features of the territory, exposure to natural phenomena, the degradation of natural resources and rapid population growth, has endangered the welfare of its population, because of the high vulnerability and risk to the occurrence of natural disasters, principally flooding from the Grande Matagalpa River, which crosses through the center of the city, frequently leaving significant material damage and human loss. In this context, the initiative from the municipal government of Matagalpa to implement the “Rio Grande Matagalpa” project was born, financed by the European Cooperation, in order to initiate a process of flood risk management in the upper river basin and to strengthen and empower the population and the community in the prevention, mitigation and preparation against floods. As part of the project, the Joint Early Warning System (EWS) was designed, implemented and is currently operating, its purpose being to have available the information in real time, provided by automated tools and equipment, and monitored by local community organizations. The EWS is integrated into the risk management process which allows it to alert and communicate to the population about the possible occurrence of phenomenon possibly causing natural disasters, principally flooding in the city of Matagalpa.

The tunnel project has a large one-time investment, a long construction period, and potential safety hazards. During the operation period, structural cracks, voids, deformation, dislocation, water leakage and other diseases may occur, which greatly reduces the use function of the tunnel and threatens its safety in construction and operation. Therefore, it is necessary to carry out the development and research of tunnel safety monitoring and early warning system to pre-perceive and predict the working state and disease characteristics of the tunnel structure and timely warn. Aiming at the working state and disease characteristics of the tunnel, this paper discusses how to obtain the dynamic changes of the working state and disease characteristics of the key parts of the tunnel in real time, and optimizes the design of the tunnel safety monitoring and early warning system. A multi-dimensional monitoring and early warning system for tunnel safety is recommended, which can control the working state and disease changes of the tunnel in real time, and improve the service quality and service life of the tunnel.