Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA

Landslide characterization applying Sentinel-1 images and InSAR technique: The Muyubao landslide in the Three Gorges Reservoir area, China

Landslides are a common natural hazard that causes casualties and unprecedented economic losses every year, especially in vulnerable developing countries. Considering the high cost of in-situ monitoring equipment and the sparse coverage of monitoring points, the Sentinel-1 images and Interferometric Synthetic Aperture Radar (InSAR) technique were used to conduct landslide monitoring and analysis. The Muyubao landslide in the Three Gorges Reservoir area in China was taken as a case study. A total of 37 images from March 2016 to September 2017 were collected, and the displacement time series were extracted using the Stanford Method for Persistent Scatterer (StaMPS) small baselines subset method. The comparison to global positioning system monitoring results indicated that the InSAR processing of the Muyubao landslide was accurate and reliable. Combined with the field investigation, the deformation evolution and its response to triggering factors were analyzed. During this monitoring period, the creeping process of the Muyubao landslide showed obvious spatiotemporal deformation differences. The changes in the reservoir water level were the trigger of the Muyubao landslide, and its deformation mainly occurred during the fluctuation period and high-water level period of the reservoir

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision- making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices

Geo-Information Technology and Its Applications

Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research

Spatial prediction of landslide susceptibility/intensity through advanced statistical approaches implementation: applications to the Cinque Terre (Eastern Liguria, Italy)

Landslides are frequently responsible for considerable huge economic losses and casualties in mountainous regions especially nowadays as development expands into unstable hillslope areas under the pressures of increasing population size and urbanization (Di Martire et al. 2012). People are not the only vulnerable targets of landslides. Indeed, mass movements can easily lay waste to everything in their path, threatening human properties, infrastructures and natural environments. Italy is severely affected by landslide phenomena and it is one of the most European countries affected by this kind of phenomena. In this framework, Italy is particularly concerned with forecasting landslide effects (Calcaterra et al. 2003b), in compliance with the National Law n. 267/98, enforced after the devastating landslide event of Sarno (Campania, Southern Italy). According to the latest Superior Institute for the Environmental Protection and Research (ISPRA, 2018) report on "hydrogeological instability" of 2018, it emerges that the population exposed to landslides risk is more than 5 million and in particular almost half-million falls into very high hazard zones. The slope stability can be compromised by both natural and human-caused changes in the environment. The main reasons can be summarised into heavy rainfalls, earthquakes, rapid snow-melts, slope cut due to erosions, and variation in groundwater levels for the natural cases whilst slopes steepening through construction, quarrying, building of houses, and farming along the foot of mountainous zone correspond to the human component. This Ph.D. thesis was carried out in the Liguria region, inside the Cinque Terre National Park. This area was chosen due to its abundance of different types of landslides and its geological, geomorphological and urban characteristics. The Cinque Terre area can be considered as one of the most representative examples of human-modified landscape. Starting from the early centuries of the Middle Ages, local farmers have almost completely modified the original slope topography through the construction of dry-stone walls, creating an outstanding terraced coastal landscape (Terranova 1984, 1989; Terranova et al. 2006; Brandolini 2017). This territory is extremely dynamic since it is characterized by a complex geological and geomorphological setting, where many surficial geomorphic processes coexist, along with peculiar weather conditions (Cevasco et al. 2015). For this reason, part of this research focused on analyzing the disaster that hit the Cinque Terre on October, 25th, 2011. Multiple landslides took place in this occasion, triggering almost simultaneously hundreds of shallow landslides in the time-lapse of 5-6 hours, causing 13 victims, and severe structural and economic damage (Cevasco et al. 2012; D\u2019Amato Avanzi et al. 2013). Moreover, this artificial landscape experienced important land-use changes over the last century (Cevasco et al. 2014; Brandolini 2017), mostly related to the abandonment of agricultural activity. It is known that terraced landscapes, when no longer properly maintained, become more prone to erosion processes and mass movements (Lesschen et al. 2008; Brandolini et al. 2018a; Moreno-de-las-Heras et al. 2019; Seeger et al. 2019). Within the context of slope instability, the international community has been focusing for the last decade on recognising the landslide susceptibility/hazard of a given area of interest. Landslide susceptibility predicts "where" landslides are likely to occur, whereas, landslide hazard evaluates future spatial and temporal mass movement occurrence (Guzzetti et al., 1999). Although both definitions are incorrectly used as interchangeable. Such a recognition phase becomes crucial for land use planning activities aimed at the protection of people and infrastructures. In fact, only with proper risk assessment governments, regional institutions, and municipalities can prepare the appropriate countermeasures at different scales. Thus, landslide susceptibility is the keystone of a long chain of procedures that are actively implemented to manage landslide risk at all levels, especially in vulnerable areas such as Liguria. The methods implemented in this dissertation have the overall objective of evaluating advanced algorithms for modeling landslide susceptibility. The thesis has been structured in six chapters. The first chapter introduces and motivates the work conducted in the three years of the project by including information about the research objectives. The second chapter gives the basic concepts related to landslides, definition, classification and causes, landslide inventory, along with the derived products: susceptibility, hazard and risk zoning, with particular attention to the evaluation of landslide susceptibility. The objective of the third chapter is to define the different methodologies, algorithms and procedures applied during the research activity. The fourth chapter deals with the geographical, geological and geomorphological features of the study area. The fifth chapter provides information about the results of the applied methodologies to the study area: Machine Learning algorithms, runout method and Bayesian approach. Furthermore, critical discussions on the outcomes obtained are also described. The sixth chapter deals with the discussions and the conclusions of this research, critically analysing the role of such work in the general panorama of the scientific community and illustrating the possible future perspectives

Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022

This open access book provides an overview of the progress in landslide research and technology and is part of a book series of the International Consortium on Landslides (ICL). It gives an overview of recent progress in landslide research and technology for practical applications and the benefit for the society contributing to understanding and reducing landslide disaster risk

Soil Erosion and Sustainable Land Management (SLM)

This Special Issue titled “Soil Erosion and Sustainable Land Management” presents 13 chapters organized into four main parts. The first part deals with assessment of soil erosion that covers historical sediment dating to understand past environmental impacts due to tillage; laboratory simulation to clarify the effect of soil surface microtopography; integrated field observation and the random forest machine learning algorithm to assess watershed-scale soil erosion assessment; and developing the sediment delivery distributed (SEDD) model for sub-watershed erosion risk prioritization. In Part II, the factors controlling soil erosion and vegetation degradation as influenced by topographic positions and climatic regions; long-term land use change; and improper implementation of land management measures are well dealt with. Part III presents different land management technologies that could reduce soil erosion at various spatial scales; improve land productivity of marginal lands with soil microbes; and reclaim degraded farmland using dredged reservoir sediments. The final part relates livelihood diversification to climate vulnerability as well as the coping strategy to the adverse impacts of soil erosion through sustainable land management implementation which opens prospects for policy formulation. The studies cover regions of Africa, Europe, North America and Asia, being dominantly conducted under the framework of international scientific collaborations through employing a range techniques and scales, from the laboratory to watershed scales. We believe those unique features of the book could attract the interest of the wider scientific community worldwide