423 research outputs found
Improving basin scale shallow landslide modelling using reliable soil thickness maps
No full textSoil thickness is a well-known factor controlling shallow landsliding. Notwithstanding, its spatial organisation over large areas is poorly understood, and in basin scale slope analyses it is often established using simple methods. In this paper, we apply five different soil thickness models in two test sites, and we use the obtained soil thickness maps to feed a slope stability model. Validation quantifies how errors in soil thickness influence the resulting factor of safety and points out which method grants the best results. In particular, in our cases, slope-derived soil thickness patterns produced the worst slope stability assessment, while the use of reliable soil thickness maps obtained by means of a more complex geomorphologically indexed model improved shallow landslides modelling. © 2011 The Author(s)
Rainfall thresholds for the forecasting of landslide occurrence at regional scale
No full textThis paper concerns a regional scale warning system for landslides that relies on a decisional algorithm based on the comparison between rainfall recordings and statistically defined thresholds. The latter were based on the total amount of rainfall, which was cumulated considering different time intervals: 1-, 2- and 3-day cumulates took into account the critical rainfall influencing shallow movements, whilst a variable time interval cumulate (up to 240 days) was used to consider the triggering of deep-seated landslides in low permeability terrains. A prototypal version of the model was initially set up to define statistical thresholds. Then, thresholds were calibrated using a database of past georegistered and dated landslides. A validation procedure showed that the calibration highly improves the results and therefore the model was integrated in the regional warning system of Emilia Romagna (Italy) for civil protection purposes. The proposed methodology could be easily implemented in other similar regions and countries where a sufficiently organised meteorological network is present. © 2011 The Author(s)
Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting
No full textLandslide susceptibility assessment using machine learning models is a popular and consolidated approach worldwide. The main constraint of susceptibility maps is that they are not adequate for temporal assessments: they are generated from static predisposing factors, allowing only a spatial prediction of landslides. Recently, some methodologies have been proposed to provide spatiotemporal landslides prediction starting from machine learning algorithms (e.g., combining susceptibility maps with rainfall thresholds), but the attempt to obtain a dynamic landslide probability map directly by applying machine learning models is still in the preliminary phase. This work provides a contribution to fix this gap, combining in a Random Forest (RF) algorithm a static indicator of the spatial probability of landslide occurrence (i.e., a classical susceptibility index) and a number of dynamic variables (i.e., seasonality and the rainfall amount cumulated over different reference periods). The RF implementation used in this work allows the calculation of the Out-of-Bag Error and depicts Partial Dependence Plots, two indices that were used to quantify the variables' importance and to comprehend if the model outcomes are consistent with the triggering mechanism observed in the case of study (Metropolitan City of Florence, Italy). The goal of this research is not to set up a landslide probability map, but to 1) understand how to populate training and test datasets with observations sampled over space and time, 2) assess which rainfall variables are statistically more relevant for the identification of the time and location of landslides, and 3) test the dynamic application of RF in a forecasting model for the spatiotemporal prediction of landslides. The proposed dynamic methodology shows encouraging results, consistent with the actual knowledge of the physical mechanism of the triggering of shallow landslides (mainly influenced by short and intense rainfalls) and identifies some benchmark configurations that represents a promising starting point for future regional-scale applications of machine learning models to dynamic landslide probability assessment and early warning
The PLANTARIO project: a useful tool for hydraulic policy, urban planning and flood risk assessment
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Technical Note: An operational landslide early warning system at regional scale based on space-time-variable rainfall thresholds
No full textWe set up an early warning system for rainfall-induced landslides in Tuscany (23 000 km2). The system is based on a set of state-of-the-art intensity-duration rainfall thresholds (Segoni et al., 2014b) and makes use of LAMI (Limited Area Model Italy) rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain gauges. The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult, and it provides different outputs. When switching among different views, the system is able to focus both on monitoring of real-time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a basic data view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain gauges can be displayed and constantly compared with rainfall thresholds. To better account for the variability of the geomorphological and meteorological settings encountered in Tuscany, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of more than 300 rain gauges, it allows for the monitoring of each alert zone separately so that warnings can be issued independently. An important feature of the warning system is that the visualization of the thresholds in the WebGIS interface may vary in time depending on when the starting time of the rainfall event is set. The starting time of the rainfall event is considered as a variable by the early warning system: whenever new rainfall data are available, a recursive algorithm identifies the starting time for which the rainfall path is closest to or overcomes the threshold. This is considered the most hazardous condition, and it is displayed by the WebGIS interface. The early warning system is used to forecast and monitor the landslide hazard in the whole region, providing specific alert levels for 25 distinct alert zones. In addition, the system can be used to gather, analyze, display, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists
GPS-based high resolution mapping techniques in the Arno river for a correct flood risk management
No full textAn extensive high-resolution GPS mapping survey has been carried out over the entire course of the Arno river within the Florence province (62km on each river’s bank) in order to create a GIS database containing all the natural, urban, hydrological and morphological elements around the Arno and its principal tributaries within the Florence urban and suburban area. The purpose of this project is to provide local public administrations with an helpful tool for managing hydrological risk, hydraulic policy and urban planning. The geodatabase includes artificial and natural elements such as railways, roads, buildings, assets, bridges, administrative boundaries, hydraulic works, drainage outlets, dikes, hydro-morphological elements (such as bars or eroding banks) and so on. All the mapped elements were georegistered and provided with alphanumerical descriptions, including the present condition of all elements, in order to plan ordinary and extraordinary maintenance works. The spatial location of the elements is very accurate (less than ±5cm error both in plan and in elevation), with special attention to the accurate positioning for all the elements of flood containment, both natural (riverbank line) and artificial (dikes or walls). Such a complete and accurate database contains all the elements connected with the fluvial dynamic and with the hydraulic risk and will provide very accurate topographic and geometric data to be used in hydrological models. Moreover, in our opinion, realistic assessments concerning hydraulic risk and flood inundation cannot be obtained only with geometrical and topographical criteria (as currently happens with the Arno river), but should also include an evaluation of the stability of the dykes, most of which are old and have been built using heterogeneous materials in the period between First and Second World War. For this reason, the database has been integrated with geotechnical analyses and numerical modelling carried out on a test site along the Arno river course in order to establish a preliminary assessment of the criteria to evaluate dikes stability at large scale. The extension of such methodology to the whole mapped Arno dikes could provide in the near future a first attempt at integrating geotechnical dikes stability schemes into hydrological models for the prediction of flood risk
An empirical geomorphology-based approach to the spatial prediction of soil thickness at catchment scale
No full textCatchment modeling in areas dominated by active geomorphologic processes, such as soil erosion and landsliding, is often hampered by the lack of reliable methods for the spatial estimation of soil depth. In a catchment, soil thickness h can vary as a function of many different and interplaying factors, such as underlying lithology, climate, gradient, hillslope curvature, upslope contributing area, and vegetation cover, making the distributed estimation of h challenging and often unreliable. In this work we present an alternative methodology which links soil thickness to gradient, horizontal and vertical slope curvature, and relative position within the hillslope profile. While the relationship with gradient and curvature should reflect the kinematic stability of the regolith cover, allowing greater soil thicknesses over planar and concave areas, the distance from the hill crest (or from the valley bottom) accounts for the position within the soil toposequence. This last parameter is fundamental; points having equal gradient and curvature can have significantly different soil thickness due to their dissimilar position along the hillslope profile. The proposed model has been implemented in a geographic information system environment and tested in the Terzona Creek basin in central Italy. Results are in good agreement with field data (mean absolute error is 11 cm with 8.5 cm standard deviation) and average errors are lower than those obtained with other topography-based methods, where mean absolute error ranges from 47 cm for a model based on curvature, position, and slope gradient to 94 cm for a model based solely on slope gradient. As a further test, the predicted soil thickness was used to determine derived quantities, such as the factor of safety for landsliding potential. Our model, when compared to other empirical methods, returns the best results and, therefore, can improve the prediction of soil losses and sediment production when utilized in conjunction with hydrological and landsliding models. Copyright © 2010 by the American Geophysical Union
Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization
No full textThe literature about landslide susceptibility mapping is rich of works focusing on improving or comparing the algorithms used for the modeling, but to our knowledge, a sensitivity analysis on the use of geological information has never been performed, and a standard method to input geological maps into susceptibility assessments has never been established. This point is crucial, especially when working on wide and complex areas, in which a detailed geological map needs to be reclassified according to more general criteria. In a study area in Italy, we tested different configurations of a random forest–based landslide susceptibility model, accounting for geological information with the use of lithologic, chronologic, structural, paleogeographic, and genetic units. Different susceptibility maps were obtained, and a validation procedure based on AUC (area under receiver-operator characteristic curve) and OOBE (out of bag error) allowed us to get to some conclusions that could be of help for in future landslide susceptibility assessments. Different parameters can be derived from a detailed geological map by aggregating the mapped elements into broader units, and the results of the susceptibility assessment are very sensitive to these geology-derived parameters; thus, it is of paramount importance to understand properly the nature and the meaning of the information provided by geology-related maps before using them in susceptibility assessment. Regarding the model configurations making use of only one parameter, the best results were obtained using the genetic approach, while lithology, which is commonly used in the current literature, was ranked only second. However, in our case study, the best prediction was obtained when all the geological parameters were used together. Geological maps provide a very complex and multifaceted information; in wide and complex area, this information cannot be represented by a single parameter: more geology-based parameters can perform better than one, because each of them can account for specific features connected to landslide predisposition
Urban planning, flood risk and public policy: The case of the Arno River, Firenze, Italy
No full textUrban planning and hydraulic risk management are a worldwide necessity which is best achieved when natural and artificial elements located closely to watercourses are known in great detail. A geodatabase is a practical tool to store and manage such information. When working at small scales, however, any well established methodology exists to map the position and the height of the various elements with centimetric accuracy. For this purpose we propose a methodology that we tested on the Arno river (Italy) and its most urbanized tributaries, a demonstrative case of hydrological risk around large fluvial systems. We surveyed 116 km of river traits to collect GPS measurements and information about all the natural and artificial elements connected to hydraulic risk and fluvial dynamics. The mapped elements include (but are not limited to) buildings, assets, bridges, hydraulic works, weirs, drainage outlets, dikes, riverbanks, structural damages, fluvial bars and eroding banks. All these elements were mapped with high accuracy, in particular a local geoid model, related only to the study area, was developed to obtain orthometric heights affected with errors ≤0.05 m. Consequently a GIS geodatabase was built to visualize the spatial distribution of the mapped elements and to store a series of technical data, including the present preservation condition for man-made objects. The geodatabase provides an overview of the territories connected with the fluvial dynamics, highlighting that in the studied territory, the more is urbanized, the more it is exposed to hydraulic risk. In a similar context, the geodatabase itself represents a useful tool for the management of the hydrological risk and for hydraulic policy and urban planning. © 2011 Elsevier Ltd
Energia pulita e rinnovabile dal fiume Arno. I risultati di uno studio di fattibilità in provincia di Firenze
No full textThe paper considers the possibility to use the transversal hydraulic works to produce locally the hydroelectric powe
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