Landslides are one of the most significant natural geological hazards, capable of causing extensive damage to lives, infrastructure, and property. These events are often triggered by specific geological and environmental conditions that can be monitored utilizing advanced technologies such as Wireless Sensor Networks (WSNs). This study introduces a novel itinerary planning approach for WSNs, employing the Fuzzy Logic-based Particle Swarm Optimization (FLPSO) technique, which integrates Fuzzy Logic and Particle Swarm Optimization methodologies. The primary objective of this approach is to minimize the energy consumption in large-scale WSNs, thereby enhancing their efficiency for landslide detection systems. The proposed method improves on traditional network grouping methods by optimizing energy usage across sensor nodes. A case study was conducted in Shiradi village, Mangalore, India, an area characterized by high annual rainfall and changing climatic patterns. Over a year, data was collected and analyzed to evaluate the system's potential for accurate landslide hazard predictions. The soil suction stress was calculated using laboratory tests, incorporating various geotechnical and unsaturated soil parameters specific to the study area. The experimental results demonstrated that energy-efficient nodes not only have a longer operational lifespan and greater adaptability to environmental changes, but also exhibit superior performance compared to current methods, with improvements of 14.15% in Packet Delivery Ratio (PDR), 11.15% in Energy Delay Product (EDP), 10.15% in Packet Loss Ratio (PLR), 22.1% in task delay, and 20.1% in throughput.

Landslides present a significant global hazard, resulting in substantial socioeconomic losses and casualties each year. Traditional monitoring approaches, such as geodetic, geotechnical, and geophysical methods, have limitations in providing early warning capabilities due to their inability to detect precursory subsurface deformations. In contrast, the acoustic emission (AE) technique emerges as a promising alternative, capable of capturing the elastic wave signals generated by stress-induced deformation and micro-damage within soil and rock masses during the early stages of slope instability. This paper provides a comprehensive review of the fundamental principles, instrumentation, and field applications of the AE method for landslide monitoring and early warning. Comparative analyses demonstrate that AE outperforms conventional techniques, with laboratory studies establishing clear linear relationships between cumulative AE event rates and slope displacement velocities. These relationships have enabled the classification of stability conditions into essentially stable, marginally stable, unstable, and rapidly deforming categories with high accuracy. Field implementations using embedded waveguides have successfully monitored active landslides, with AE event rates linearly correlating with real-time displacement measurements. Furthermore, the integration of AE with other techniques, such as synthetic aperture radar (SAR) and pore pressure monitoring, has enhanced the comprehensive characterization of subsurface failure mechanisms. Despite the challenges posed by high attenuation in geological materials, ongoing advancements in sensor technologies, data acquisition systems, and signal processing techniques are addressing these limitations, paving the way for the widespread adoption of AE-based early warning systems. This review highlights the significant potential of the AE technique in revolutionizing landslide monitoring and forecasting capabilities to mitigate the devastating impacts of these natural disasters.

This article outlines a methodology for assessing landslide susceptibility and puts forth a monitoring and alert system based on computational modeling. The highlighted area is notorious region for recurring landslide incidents, resulting in both material and occasionally human losses due to extensive human settlement. Employing finite element analysis (FEA) and the limit equilibrium method (LEM) a comprehensive approach to evaluate landslide susceptibility in the specified region was developed. The analyses, incorporating stress and strain considerations and determining the factor of safety, were integrated with rainfall-induced infiltrations. The introduction of soil mass creep concepts into computational analyses aids in establishing alert and emergency thresholds for both horizontal and vertical displacements, both on the surface and at depth. Instruments were recommended for reading these parameters, generating a continuous on-site monitoring associated to several levels of alerts. A sensitivity analysis with variations in the friction angle, elastic modulus and permeability values of the unstable soil mass was performed in order to parametrically evaluate these parameters influence.

Assessing structural foundation damage following an earthquake is critical for safety evaluation. However, assessing the damage to pile foundations with traditional visual inspections and non-destructive testing methods is challenging. This study evaluated the use of acoustic emission (AE) monitoring for damage detection and location in foundation structures during earthquake simulations by conducting shaking table experiments. Scaled models of foundation structures with and without surrounding soil were used in the experiments, and the performance of the AE monitoring system was evaluated by comparing the AE parameters via visual inspection. The experimental results showed that the AE monitoring system could effectively predict the initiation of cracks in foundation structures that experienced an earthquake. In addition, an appropriate filtering criterion for the shaking table experiments was established based on the AE characteristics of the foundation structures during the earthquake simulation, thereby improving the performance of the AE monitoring system for damage location. Consequently, this study contributed to a better understanding of the applicability of AE monitoring systems to foundation structures during earthquakes. (c) 2024 Production and hosting by Elsevier B.V. on behalf of The Japanese Geotechnical Society. This is an open access article under the CC BY- NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Occurrence of loess landslide has been more frequent due to the drastic global climate change, rapid expansion of human disturbances and continuous intensification of engineering activities. The activation and evolution mechanisms of the loess landslides under the rainfall are yet to be studied. In this paper, with reference to the Yangpoyao slope with seepage fissures under rainfall, an adjustable-angle landslide model test system is developed, integrating the rainfall simulation system, the measurement system and the data acquisition system, and the deformation development of the model, the rainfall infiltration, the change of water content and the destructive process of the model are monitored by the monitoring technology of multi-means and multi-methods throughout the course of the disaster. A distributed fibre-optic sensor system with the characteristics of continuity and high precision is used to monitor the temperature and strain within the slope model. The deformation evolution mechanism of fissured loess slopes under rainfall was elucidated through the observation of experimental phenomena and the analysis of the internal strain values of the soil, as measured by fibre optic sensors. The experimental results show that the collapse process of loess slopes can be categorised into three types, i.e. sinkhole collapse, block collapse and gully collapse, and that the deformation and damage patterns of the loess landslide model are mainly caused by shallow soil movement induced by erosion. Through the comparative analysis of the model test and the photographs of the field investigation, it is further demonstrated that the damage pattern shown in the physical model test is basically consistent with the slope condition of the real Yangpoyao slope, which provides a new theoretical reference for natural disaster prediction and management of loess slopes and landslides.

Yield data represent a valuable layer for supporting decision-making as they reflect crop management results. Forestry decision-makers often rely on coarse spatial resolution data (e.g., forest inventory plots) despite the availability of modern harvesters that can provide high-resolution forestry yield data. The objectives of this study were to present a method for generating high-resolution Eucalyptus grandis yield data (individual tree-level) and explore their applications, such as correlation analysis with soil attributes to aid nutrient recommendations. Two evaluations were conducted at two sites in Brazil: (a) assessing the positioning accuracy of the global navigation satellite system (GNSS) receiver positioning, and (b) analyzing the yield data and their correlation with the soil attributes. The results indicated that positioning the GNSS receiver at the harvesting head provided higher accuracy than placement at the top of the harvester cabin for individual tree-level data. Reliable yield data were generated despite the GNSS receiver's increased susceptibility to damage when mounted on a harvest head. The linear correlation analysis between the Eucalyptus grandis yield data and soil attributes showed both negative (Clay, B, S, coarse sand, and potential acidity - H + Al) and positive correlations (K, Mg, pH-SMP, Ca, sum of bases, pH, base saturation, fine sand, total sand, and silt content). This study demonstrates the feasibility of obtaining high-resolution yield data at the individual tree-level and their correlation with soil attributes, providing valuable insights for improving forestry decision-making.

- This research proposes a solution to improve the system for monitoring relevant environmental parameters using sensors for flood mitigation. Sensors are used to collect data regarding farm flood situation. The collected data are trained for a classification model to activate the solar-powered water pump to mitigate flood incidents in a flood-prone area. The system helps farmers to monitor real-time environmental parameters relevant to farming operations and flood including soil moisture level, water level, and water flow speed in a nearby canal that provides water to the farm. To reduce flood damage, the system assists to drain the excessive water to prevent prolonged submerging of the crop. The devices are designed to use the electricity from solar power, so the system is practically used outdoor where an electricity cord is difficult to setup. Experimental results show that the sensing data from the deployed sensors are accurate. The generated prediction models give the high performance with average of 1.0, 0.97, and 0.93 F-1 score for no-flooding, mild-flooding, and severe flooding respectively.

This paper presents an on -going development of Internet-of-Things (IoT) slope monitoring for landslide early warning system in Thailand. The current system employs a variety of sensors, namely MEMs-based tensiometers, piezometers, soil moisture sensor, tiltmeter, in-placed inclinometer and tipping bucket raingauge, all connected to Arduino-based microcontroller which relied on Narrowband, NB-IoT, protocol for data transmission to the cloud server. A specially designed application platform was developed to convert the sensor readings to engineering unit and ultimately geotechnical parameters, such as factor of safety, which enable engineers to readily understand the situation and make an informed-decision based on such parameters. A weighted approach was proposed in calculating the overall landslide hazard level based various kinds of sensor readings. A case history of Kratu-Patong Road Landslide in Phuket, Southern Thailand, taking place in Year 2022 was presented to demonstrate how the developed IoT system was used real -time together with geotechnical analysis to aid in traffic management during the critical time. The warning event primarily stemmed from spikes in slope movement, spurred by heightened traffic intensity. Rapid slope movement during the incident was characterized by a tilting magnitude of -2 to 1.2 degrees and a velocity ranging from -1.7 to 1.8 degrees per hour. Notably, the calculation of the warning index based on tilting magnitude provides a continuous warning message, in contrast to an intermittent message based on tilting velocity. The tensiometer effectively detected the decrease in suction caused by slope movement, while the piezometer only registered changes in pore-water pressure when the groundwater table rose above the measurement point. Finally, an Artificial Neural Network (ANN) model was used to predict the pore-water pressure at different depths based on 5 rainfall parameters, namely, 5 -min, 1-hour, 1 -day, 3 -day and 7 -day antecedent rainfalls. The model demonstrated satisfactory predictive accuracy (R 2 = 0.644, RMSE = 3.637 kPa), offering promising potential for integration with the IoT platform in the future.

Permafrost occurrence and distribution are highly dependent on climatic conditions. In Antarctica, permafrost occurs in deglaciated areas; it is usually associated with vegetation communities. A monitoring program based on the sensitive system vegetation-permafrost will allow to detect climate change effects. In this paper we propose a research protocol to monitor active layer changes and vegetation development through the phytosociological approach. In each study site different types of vegetation-pennafrost systems have to be analysed to calibrate climate change effects both within the same study site and along the transect, thus avoiding the interference of local processes. A first step of the protocol has been realised at Jubany, King George Island, where 5 permanent plots have been installed in 2001. The next steps will be the creation of a monitoring site in Terra Nova Bay for Continental Antarctica and on Signy Island for Maritime Antarctica.