Generalized Model of Real-Time Deformation Measurements and its Experimental Verification

Technology development enabled real-time monitoring of terrain and artificial structures through the use of the systems comprised of geodetic and geotechnical sensors. Although the structure and the architecture of those systems can vary, they have many common features: automated measurements which provide monitoring and alarming when tolerances are exceeded, systems robustness involving hot-swapping sensors, its redundancy and error detection. Also, they can be structured and configured to achieve desired functionality and performance. Generalized model of real-time deformation measurements, presented in this paper, involves these common characteristics. The model is presented as a flowchart, and later applied within an experiment carried out in laboratory environment. The established system includes geodetic and geotechnical sensors and measurements are done on a physical model of a landslide. Several functions included in generalized model were implemented in the experiment. Results show that system designed using the proposed model can provide required functionality, accuracy, robustness and configurability. Aim of the paper is to propose a general procedure which, with minor modifications, can be applied as a starting point in designing various systems for monitoring landslides, bridges, high buildings etc., and which overcomes some limitations that can be found in commercial software solutions

Remote Sensing Approaches and Related Techniques to Map and Study Landslides

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales

Landslide susceptibility mapping using remote sensing data and geographic information system-based algorithms

Whether they occur due to natural triggers or human activities, landslides lead to loss of life and damages to properties which impact infrastructures, road networks and buildings. Landslide Susceptibility Map (LSM) provides the policy and decision makers with some valuable information. This study aims to detect landslide locations by using Sentinel-1 data, the only freely available online Radar imagery, and to map areas prone to landslide using a novel algorithm of AB-ADTree in Cameron Highlands, Pahang, Malaysia. A total of 152 landslide locations were detected by using integration of Interferometry Synthetic Aperture RADAR (InSAR) technique, Google Earth (GE) images and extensive field survey. However, 80% of the data were employed for training the machine learning algorithms and the remaining 20% for validation purposes. Seventeen triggering and conditioning factors, namely slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, Normalized Difference Vegetation Index (NDVI), rainfall, land cover, lithology, soil types, curvature, profile curvature, Stream Power Index (SPI) and Topographic Wetness Index (TWI), were extracted from satellite imageries, digital elevation model (DEM), geological and soil maps. These factors were utilized to generate landslide susceptibility maps using Logistic Regression (LR) model, Logistic Model Tree (LMT), Random Forest (RF), Alternating Decision Tree (ADTree), Adaptive Boosting (AdaBoost) and a novel hybrid model from ADTree and AdaBoost models, namely AB-ADTree model. The validation was based on area under the ROC curve (AUC) and statistical measurements of Positive Predictive Value (PPV), Negative Predictive Value (NPV), sensitivity, specificity, accuracy and Root Mean Square Error (RMSE). The results showed that AUC was 90%, 92%, 88%, 59%, 96% and 94% for LR, LMT, RF, ADTree, AdaBoost and AB-ADTree algorithms, respectively. Non-parametric evaluations of the Friedman and Wilcoxon were also applied to assess the models’ performance: the findings revealed that ADTree is inferior to the other models used in this study. Using a handheld Global Positioning System (GPS), field study and validation were performed for almost 20% (30 locations) of the detected landslide locations and the results revealed that the landslide locations were correctly detected. In conclusion, this study can be applicable for hazard mitigation purposes and regional planning

Landslides

Landslides - Investigation and Monitoring offers a comprehensive overview of recent developments in the field of mass movements and landslide hazards. Chapter authors use in situ measurements, modeling, and remotely sensed data and methods to study landslides. This book provides a thorough overview of the latest efforts by international researchers on landslides and opens new possible research directions for further novel developments

10. 研究成果

10.1 研究成果の概要 [492]10.2 研究成果リスト一覧 [493

Lessons for Sustainable Urban Development: Interplay of Construction, Groundwater Withdrawal, and Land Subsidence at Battersea, London

The capacity of aquifers to store water and the stability of infrastructure can each be adversely influenced by variations in groundwater levels and subsequent land subsidence. Along the south bank of the River Thames, the Battersea neighbourhood of London is renovating a vast 42-acre (over 8 million sq ft) former industrial brownfield site to become host to a community of homes, shops, bars, restaurants, cafes, offices, and over 19 acres of public space. For this renovation, between 2016 and 2020, a significant number of bearing piles and secant wall piles, with diameters ranging from 450 mm to 2000 mm and depths of up to 60 m, were erected inside the Battersea Power Station. Additionally, there was considerable groundwater removal that caused the water level to drop by 2.55 ± 0.4 m/year between 2016 and 2020, as shown by Environment Agency data. The study reported here used Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology to analyse the associated land movement for Battersea, London, during this period. The average land subsidence was found to occur at the rate of −6.8 ± 1.6 mm/year, which was attributed to large groundwater withdrawals and underground pile construction for the renovation work. Thus, this study underscores the critical interdependence between civil engineering construction, groundwater management, and land subsidence. It emphasises the need for holistic planning and sustainable development practices to mitigate the adverse effects of construction on groundwater resources and land stability. By considering the Sustainable Development Goals (SDGs) outlined by the United Nations, particularly Goal 11 (Sustainable Cities and Communities) and Goal 6 (Clean Water and Sanitation), city planners and stakeholders can proactively address these interrelated challenges

Study of groundwater properties and behaviour using geospatial techniques

Groundwater contributes a significant proportion of the earth’s freshwater and is essential to sustain life on earth, but its availability in spatial and temporal dimensions is not uniform. With the advent of efficient pumps and rural electrification, global groundwater extraction increased from 312 km3/year in the 1960s to 800 km3/year in 2000s; approximately 70% of this extraction is used for agriculture. About half of domestic human water consumption in urban areas is from groundwater. The ever-increasing dependence on groundwater has led to its depletion across various parts of the world. This trend must be reversed to sustain the critical role of groundwater. Groundwater monitoring based on validated data can provide information that can guide decision making to decrease groundwater stress on local and global scales. This thesis aims to monitor spatio-temporal changes in groundwater and related phenomena (like land subsidence) using geospatial techniques like InSAR, GRACE, GIS, data analysis and data visualisation. The over-extraction or rebound of groundwater can lead to land deformation because of the change in effective stress of underground sediments. Groundwater-induced land movement can cause damage to property and resources, and hence it must be monitored for the safety and economics of a city. This thesis explores the suitability of Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) to measure land deformation and different senor-software for InSAR processing. The groundwater quantity variation and resulting land deformation for London using InSAR and Gravity Recovery and Climate Experiment (GRACE) between 2002-2010 were analysed. Long-term, decreasing, complex, non-linear patterns in the spatial and temporal domains from both InSAR and GRACE datasets were observed. The land movement velocities varied from -6 to +6 mm/year, and their reliability was validated with observed GNSS data by conducting a two-sample t-test. The average groundwater loss estimated from GRACE was found to be 9.003 MCM/year. The results demonstrate that InSAR and GRACE complement each other and can be an excellent source of monitoring groundwater for hydrologists. Then groundwater induced subsidence for London and the National Capital Territory of Delhi (NCT-Delhi) between 2016 and 2020 were studied. The land movement velocities were found to vary between -24 mm/year to +24 mm/year for London and between -18 mm/year to +30 mm/year for NCT-Delhi. This land movement was compared with observed groundwater levels and spatio-temporal variation of groundwater. A 1-D mathematical model was used to quantify land deformation for a given change in groundwater level. It was broadly observed that when large volumes of groundwater are extracted, it leads to land subsidence, and when groundwater is recharged, surface uplift is witnessed. However the local geology, did play an important role in the extent of subsidence, which was considered in the mathematical model. The increased pressure on groundwater can cause spatio-temporal changes in its quality because of various atmospheric stimulations, varied geology, variation in subsurface mineralogy and factors controlling residence times. Moreover, the variation of groundwater quality is vital for the sustainable management and safety of groundwater. Thus, the variation in groundwater quality is analysed from observed data for London between 2000 and 2020. The data samples were used from 500 wells in the London basin, and the data is provided in the free open access domain by Environment Agency. The overall groundwater in London was found to be dominant magnesium bicarbonate type which typically represents shallow fresh groundwater, and spatio-temporal variations of hardness, sodium, and dissolved oxygen (DO) were also studied. Significant variations in the range of each constituent were found, which was attributed to variation in the geology of the London Palaeogene aquifers and anthropogenic activities. All the case studies help better understand the phenomenon of spatio-temporal variation in groundwater behaviour and associated land deformation for urban cities. The research presented in this thesis can be used to determine whether groundwater is available and suitable for its intended purpose, discover pollutants, examine any spatio-temporal variations, and monitor land subsidence