This work presents the outcomes of the geomorphological investigation and mapping carried out within the Cinque Terre National Park (38 km2), an outstanding example of a human-modified landscape. Over the centuries, the natural landscape of Cinque Terre has been almost completely modified by slope terracing for agricultural purposes. Extensive field surveys, topographic maps examination and aerial photographs interpretation have led to the production, within a GIS environment, of a new geomorphological map at 1:18,000 scale which outlines the main genesis and related landforms and deposits: (i) gravity-induced features, (ii) fluvial and runoff features, (iii) coastal features and (iv) anthropogenic features. Special attention has been given to the mapping of terraced slopes, which at present are extremely vulnerable to gravity-driven processes and running water because of extensive farmland abandonment. The output map is a fundamental basis for future activities of hazard assessment and zonation and the definition of land management strategies.
In most countries, landslides have caused severe socioeconomic impacts on people, cities, industrial establishments, and lifelines, such as highways, railways, and communication network systems. Socioeconomic losses due to slope failures are very high and they have been growing as the built environment expands into unstable hillside areas under the pressures of growing populations. Human activities as the construction of buildings, transportation routes, dams, and artificial canals have often been a major factor for the increasing damage due to slope failures. When recovery actions are not durable from an economic point of view, increasing the population’s awareness is the key strategy to reduce the effects of natural and anthropogenic events. Starting from the case study of the Pan-American Highway (the Ecuadorian part), this article shows a multi-approach strategy for infrastructure monitoring. The combined use of (i) DInSAR technique for detection of slow ground deformations, (ii) field survey activities, and (iii) the QPROTO tool for analysis of slopes potentially prone to collapse allowed us to obtain a first cognitive map to better characterize 22 km of the highway between the cities of Cuenca and Azogues. This study is the primary step in the development of a landslide awareness perspective to manage risk related to landslides along infrastructure corridors, increasing user safety and providing stakeholders with a management system to plan the most urgent interventions and to ensure the correct functionality of the infrastructure.
Abstract Maps that attempt to predict landslide occurrences have essentially stayed the same since 1972. In fact, most of the geo-scientific efforts have been dedicated to improve the landslide prediction ability with models that have largely increased their complexity but still have addressed the same binary classification task. In other words, even though the tools have certainly changed and improved in 50 years, the geomorphological community addressed and still mostly addresses landslide prediction via data-driven solutions by estimating whether a given slope is potentially stable or unstable. This concept corresponds to the landslide susceptibility, a paradigm that neglects how many landslides may trigger within a given slope, how large these landslides may be and what proportion of the given slope they may disrupt. The landslide intensity concept summarized how threatening a landslide or a population of landslide in a study area may be. Recently, landslide intensity has been spatially modeled as a function of how many landslides may occur per mapping unit, something, which has later been shown to closely correlate to the planimetric extent of landslides per mapping unit. In this work, we take this observation a step further, as we use the relation between landslide count and planimetric extent to generate maps that predict the aggregated size of landslides per slope, and the proportion of the slope they may affect. Our findings suggest that it may be time for the geoscientific community as a whole, to expand the research efforts beyond the use of susceptibility assessment, in favor of more informative analytical schemes. In fact, our results show that landslide susceptibility can be also reliably estimated (AUC of 0.92 and 0.91 for the goodness-of-fit and prediction skill, respectively) as part of a Log-Gaussian Cox Process model, from which the intensity expressed as count per unit (Pearson correlation coefficient of 0.91 and 0.90 for the goodness-of-fit and prediction skill, respectively) can also be derived and then converted into how large a landslide or several coalescing ones may become, once they trigger and propagate downhill. This chain of landslide intensity, hazard and density may lead to substantially improve decision-making processes related to landslide risk.
Sinkholes are widespread phenomena on Earth and represent a significant hazard to the population residing in areas affected by such events. They consist of vertical depressions with a three-dimensional funnel shape, often triggered by dissolution or erosion of geological materials. To define sinkholes susceptibility for the city of Naples (Italy), a presence-only Machine Learning technique, namely Maximum Entropy, has been applied to the study area. The result obtained consists of a sinkhole susceptibility map that highlights a relatively important extension (4.9%) of the most susceptible class which consolidates the necessity of studying such phenomena in an effort to help the government officials for selecting and applying mitigation measures and adaptation plan.
Landslide susceptibility maps are vital tools used by decision-makers to adopt mitigation strategies for future calamities. In this context, research on landslide susceptibility modelling has become a topic of relevance and is in constant evolution. Though various machine-learning techniques (MLTs) have been identified for landslide susceptibility modelling, the uncertainty inherent in the models is rarely considered. The present study attempts to quantify the uncertainty associated with landslide prediction models by developing a new methodological framework based on the ensembles of the eight MLTs. This methodology has been tested at the highlands of the southern Western Ghats region (Kerala, India), where landslides have frequently been occurring. Fourteen landslide conditioning factors have been identified as part of this study, and their association was correlated with 671 historic landslides in the study area. The study used four ensemble models such as the mean of probabilities, the median of probabilities, the weighted mean of probabilities, and the committee average. The weighted mean of probability was proved to be the best model based on the average of 800 standalone MLTs, viz., receiver operating characteristics, true skill statistics, and area under curve with corresponding validation scores. Thereafter, an uncertainty analysis was carried out on the coefficient of variation. A confident map was generated to represent the distinct zonation of landslide susceptibility areas with definite uncertainty scales. Nearly 74% of the past landslides fall in the higher susceptibility-low uncertainty category. It is also inferred that such micro-level zonation based on MLTs may improve the efficiency of landslide susceptibility maps and may help in accurately identifying landslide-prone areas in the future. The confident maps thus generated can be used as a ready reference to the planners for the formulation of landslide adaptation strategies at micro-scales.
Abstract In recent decades, developing countries have experienced an increase in the impact of natural disasters due to ongoing climate change and the sustained expansion of urban areas. The intrinsic vulnerability of settlements, due to poverty and poor governance, as well as the lack of tools for urban occupation planning and mitigation protocols, has made such impacts particularly severe. Cuenca (Ecuador) is a significant example of a city that in recent decades has experienced considerable population growth (i.e. exposure) and an associated increase in loss due to landslide occurrence. Despite such effects, updated urban planning tools are absent, so an evaluation of multitemporal exposure to landslides and related risks is required. In this perspective, a potential urban planning tool is presented based on updated data depicting the spatial distribution of landslides and their predisposing factors, as well as population change between 2010 and 2020. In addition, a multitemporal analysis accounting for changes in exposure between 2010 and 2020 and an estimation of relative landside risk was carried out. Due to the absence of spatially distributed population data, energy supply contract data have been used as a proxy of the population. The results show that the current higher exposure and related relative risk are estimated for parishes ( parroquias ) located in the southern sector of the study area (i.e. Turi , Santa Ana , Tarqui, Nulti, Baños and Paccha ). Moreover, the exposure multitemporal analysis indicates that most parishes located in the hilly areas bounding the city centre (i.e. Sayausi , San Joaquin , Tarqui , Sidcay , Baños , Ricaurte , Paccha and Chiquintad ) are experiencing sustained population growth and will be potentially exposed to an increased risk with a consistently growing trend. The obtained relative risk map can be considered a valuable tool for guiding land planning, land management, occupation restriction and early warning strategy adoption in the area. The methodological approach used, which accounts for landslide susceptibility and population variation through proxy data analysis, has the potential to be applied in a similar context of growing population cities in low- to mid-income countries, where data usually needed for a comprehensive landslide risk analysis are non-existing or only partially available.
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