Landslide susceptibility mapping using machine learning: A literature survey

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.Web of Science1413art. no. 302

A new integrated approach for landslide data balancing and spatial prediction based on generative adversarial networks (GAN)

Landslide susceptibility mapping has significantly progressed with improvements in machine learning techniques. However, the inventory / data imbalance (DI) problem remains one of the challenges in this domain. This problem exists as a good quality landslide inventory map, including a complete record of historical data, is difficult or expensive to collect. As such, this can considerably affect one’s ability to obtain a sufficient inventory or representative samples. This research developed a new approach based on generative adversarial networks (GAN) to correct imbalanced landslide datasets. The proposed method was tested at Chukha Dzongkhag, Bhutan, one of the most frequent landslide prone areas in the Himalayan region. The proposed approach was then compared with the standard methods such as the synthetic minority oversampling technique (SMOTE), dense imbalanced sampling, and sparse sampling (i.e., producing non-landslide samples as many as landslide samples). The comparisons were based on five machine learning models, including artificial neural networks (ANN), random forests (RF), decision trees (DT), k-nearest neighbours (kNN), and the support vector machine (SVM). The model evaluation was carried out based on overall accuracy (OA), Kappa Index, F1-score, and area under receiver operating characteristic curves (AUROC). The spatial database was established with a total of 269 landslides and 10 conditioning factors, including altitude, slope, aspect, total curvature, slope length, lithology, distance from the road, distance from the stream, topographic wetness index (TWI), and sediment transport index (STI). The findings of this study have shown that both GAN and SMOTE data balancing approaches have helped to improve the accuracy of machine learning models. According to AUROC, the GAN method was able to boost the models by reaching the maximum accuracy of ANN (0.918), RF (0.933), DT (0.927), kNN (0.878), and SVM (0.907) when default parameters used. With the optimum parameters, all models performed best with GAN at their highest accuracy of ANN (0.927), RF (0.943), DT (0.923) and kNN (0.889), except SVM obtained the highest accuracy of (0.906) with SMOTE. Our finding suggests that RF balanced with GAN can provide the most reasonable criterion for landslide prediction. This research indicates that landslide data balancing may substantially affect the predictive capabilities of machine learning models. Therefore, the issue of DI in the spatial prediction of landslides should not be ignored. Future studies could explore other generative models for landslide data balancing. By using state-of-the-art GAN, the proposed model can be considered in the areas where the data are limited or imbalanced

Landslide Assessment and Hazard Zonation in the Birbir Mariam District, Gamo Highlands, Rift Valley Escarpment, Ethiopia

The current research focused on landslide assessment and hazard zonation in the Birbir Mariam district of the Gamo highlands. The study examined landslide causative factors and used the slope susceptibility evaluation parameter to create a landslide hazard zonation covering an area of 110 km2. The landslide hazard zonation was classified using facet-wise observation. As a result, the intrinsic and external causal parameters of score schemes have been held responsible for slope instability. Inherent causative elements consist of slope geometry, slope material (rock/soil), structural discontinuities, land use/land cover, and groundwater conditions. Rainfall and human interest have seemed as external elements. The intrinsic and external triggering elements for every facet (a total of 106) were rated for their contribution to slope instability. Finally, an evaluated landslide hazard value was calculated and classified into three landslide hazard classes. According to the findings, the area has a high hazard zone of 18.87% (20.76 km2), a moderate hazard zone of 54.72% (60.19 km2), and a low hazard zone of 26.41% (29.05 km2)

EVALUATING THE INFLUENCE OF SPATIAL RESOLUTION ON LANDSLIDE DETECTION: A CASE STUDY IN THE CARLYON BEACH PENINSULA, WASHINGTON

Landslides are geological events in which masses of rock and soil slide down the slope of a mountain or hillside. They are influenced by topography, geology, weather, and human activity, and can cause extensive damage to the environment and infrastructure, as well as delay transportation networks. Therefore, it is imperative to detect early-warning signs of landslide hazards as a means of prevention. Traditional landslide surveillance consists of field mapping, but the process is costly and time consuming. Modern landslide mapping uses Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) and sophisticated algorithms to analyze surface roughness and extract spatial features and patterns of landslide and landslide-prone areas. This study follows a previous study performed that demonstrated that it is possible to detect unstable terrain using algorithmic mapping techniques. The focus of this study is to show how spatial resolution can influence the accuracy of the classification results. The DEM data was resampled from 6 to 12, 24, 48 and 96 ft spatial resolution. The surface feature extractors employed (local topographic range, local topographic variability, slope, and roughness) are fused and analyzed simultaneously by applying k-means and Gaussian Mixture Model (GMM) clustering methods. When compared with the detailed, independently compiled landslide reference map, our data shows a decrease in performance as spatial resolution decreases. These results suggest that spatial resolution does impact the performance of landslide classification

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

Modelagem de suscetibilidade e de limiares de precipitação para deslizamentos de terra utilizando métodos de aprendizagem de máquina

O Brasil é o país na América Latina com maior número de deslizamentos fatais provocados por precipitação. Neste trabalho, modela-se a suscetibilidade e os limiares de precipitação antecedente espacializados para ocorrência de deslizamentos de terra, a partir do desenvolvimento e aplicação de modelos baseados em Redes Neurais Artificiais (RNA). A modelagem é feita no âmbito da unidade geomorfológica da Serra Geral, com base em seis eventos passados cujas cicatrizes foram mapeadas com base em imagens de sensoriamento remoto. Os atributos do terreno utilizados como variáveis de entrada dos modelos foram obtidos a partir de um Modelo Digital de Elevação (MDE). O uso dos atributos reprojetados sobre os oito primeiros Componentes Principais acelerou o treinamento das RNAs, mas diminuiu a performance dos modelos. A pesquisa de métodos para a escolha dos locais para a extração das amostras de não-ocorrência proporcionou orientação importante para a composição da amostragem de treinamento dos modelos. O mapeamento da suscetibilidade também foi executado utilizando outros dois métodos de Aprendizagem de Máquina, Sistemas de Inferência Difusos (Fuzzy) (FIS) e Florestas Aleatórias, com bons resultados. Por fim, foram modelados conjuntamente a suscetibilidade a deslizamentos e os limiares de precipitação para a região de estudo, utilizando RNAs de múltiplas saídas treinadas com validação cruzada espacial, com resultados satisfatórios (AUC = 0,90, MEA = 32,77 mm, representando 25,99%). A transferibilidade do modelo de suscetibilidade foi analisada em uma bacia na mesma formação cujos dados não foram utilizados na modelagem, apresentando um AUC de 0,96.Brazil is the Latin American country that concentrates the highest number of deadly rainfall-induced landslides. In this Thesis, landslide susceptibility and spatialized precipitation thresholds for landslide occurrence are modeled based on the development and application of Artificial Neural Networks (ANN). The area of study is the Serra Geral geomorphological unit. The modeling is based on six past events from which scars were mapped based on remote sensing imagery. The terrain attributes used as input variables for the models were obtained from a Digital Elevation Model (DEM). The use of the first eight attributes that were reprojected on Principal Components accelerated the training of the ANNs but decreased the performance of the models. Methods for non-landslide sample selection were investigated, and the results obtained were an important base for composing the training samples in the remaining of this Thesis. Landslide susceptibility was modeled through two other Machine Learning methods, namely Fuzzy Inference Systems (FIS) and Random Forests, attaining good performance. Finally, we modeled the landslide susceptibility and precipitation thresholds concurrently for the entire study region using multiple-output ANNs trained with spatial cross-validation, with satisfactory results (AUC = 0.90, MEA = 32.77 mm, representing 25.99%). The transferability of the susceptibility model was analyzed in a basin within the same geological formation, from which data was not previously acquired for the models, attaining an AUC of 0.96

Socio-Environmental Vulnerability Assessment for Sustainable Management

This Special Issue explores the cross-disciplinary approaches, methodologies, and applications of socio-environmental vulnerability assessment that can be incorporated into sustainable management. The volume comprises 20 different points of view, which cover environmental protection and development, urban planning, geography, public policymaking, participation processes, and other cross-disciplinary fields. The articles collected in this volume come from all over the world and present the current state of the world’s environmental and social systems at a local, regional, and national level. New approaches and analytical tools for the assessment of environmental and social systems are studied. The practical implementation of sustainable development as well as progressive environmental and development policymaking are discussed. Finally, the authors deliberate about the perspectives of social–environmental systems in a rapidly changing world

Geo-Environmental Approaches for the Analysis and Assessment of Groundwater Resources at Catchment-Scale

This book focuses on the tools and methods used for tackling the complexity of the different hydrological and hydrogeological set-ups, the hydrodynamic patterns, the site specifications, and the wide variability of internal and external factors and/or processes on the catchment-scale level that impose the need for combined integrated approaches of robust methods. This Special Issue aims to provide successful applications or new insights on the stand-alone or joint considerations of groundwater resources assessment and characterization methods and explore new state-of-the-art methodological concepts in light of a rapidly changing environment