1,721,052 research outputs found
High resolution mapping for the management of the fluvial dynamics in intensely urbanized areas
No full textHigh resolution mapping for the management of the fluvial dynamics in intensely urbanized area
An attempt to increase the geological information in landslide susceptibility mapping and sensitivity to different geological parameters.
No full textGeological maps convey different and multifaceted information including lithology, age, tectonism and so on. This complex information is not fully exploited in landslide susceptibility (LS) studies, as a single parameter is usually derived from the geological map of the study area (e.g. the area is divided into lithological or lithostratigraphic or geological units). The aim of this work is testing different approaches to extract significant information from geological maps, creating different parameterizations, and analyzing the sensitivity of a LS model to these variations.
Our test site is a 3100 km2 wide area in Tuscany (Italy) characterized by a very complex geological setting. A 1:10000 scale geological map subdivides the area into 194 different lithostratigraphic units. This map was reclassified according to different criteria, creating 6 different parameters derived from the same geological map: lithology (6 lithological classes), age of deposition (the area was subdivided into 6 chronological units), paleogeography (6 units were differentiated on the basis of their environment of formation), genesis of the bedrock (5 classes accounted for the mechanism of formation of the outcropping rock/terrain), broad tectonic domain (the mapped elements were grouped into 5 broad structural units accounting for their tectonic history), detailed tectonic domain (same as before but with a more detailed subdivision into 10 classes).
Some of these parameters have already been used in LS studies, others have been used here for the first time; however, all of them have some connections with landslide predisposition. These parameters were used (one by one and altogether) to run seven times a landslide susceptibility model based on the widely used random forest machine learning algorithm. The model configurations and resulting maps were evaluated in terms of AUC(Area Under Curve) and OOBE(out of bag error): while the former expresses the forecasting effectiveness of each configuration, the latter expresses, among a single configuration, the importance of each input parameter.
We discovered that the results are very sensitive to the approach used to consider geology in the susceptibility assessment, with AUC values ranging from 63.5% (using chronological units) to 70.0% (using genetic units) and 75.2% (using all the geology-derived parameters simultaneously). These results are in line with OOBE statistics, which showed a similar relative importance of the geologically-driven parameters.
These outcomes can to assist future landslide susceptibility studies for different reasons:
(i)at least in our study area, lithology, which is commonly used in LS, did not provide the best results;
(ii)as geological maps provide multifaceted information, a single classification approach cannot fully grasp this complexity; therefore, the best results can be obtained using different geology-based parameters simultaneously, because each of them can account for specific features connected to landslide predisposition (to our knowledge, a similar approach has never been attempted before in LS literature).
(iii)When using thematic maps to feed LS models, it is important to fully understand the nature and the meaning of the information provided by the geology-related maps: results are very sensitive to this kind of information and the interpretation of the results should take it into account
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
Optimization of rainfall thresholds for landslide early warning through false alarm reduction and a multi-source validation
Full text linkLandslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization
Full text linkThe 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
Spatial modeling of pyroclastic cover deposit thickness (depth to bedrock) in peri-volcanic areas of Campania (southern Italy)
Full text linkIn this study, the main focus is the application and improvement of four empirical models, which account for the pyroclastic cover deposit thickness (PCDT) spatial distribution with respect to the bedrock surrounding the Somma-Vesuvius volcano(Campania, southern Italy). Three models, which are already known in the literature, link the depth to bedrock to the morphologicalfeatures of a slope. An original model called SEPT (slope exponential pyroclastic thickness) is presented in this manuscript and combinesthe initial total thickness of ash-fall pyroclastic cover with the slope gradient. All models were applied and validated using field measurements derived from this and preceding studies in the study area. The main finding is that the spatial distribution of the depth to bedrock in mountainous peri-volcanic areas mainly depends on the initial thickness of air-fallen material at a given position and slope angle. These findings allowed for the recognition of an ash-fall pyroclastic depositional environment that is characterized by different processes from those existing in other geomorphological frameworks and in which the soil thickness along the slopes is controlled by the weathering of bedrock and the formation of soilin situ. Finally, in this research, a reliable approach is proposed that is also applicable to other peri-volcanic areas of the world to assess the spatial distribution of the depth to bedrock, which is a fundamentally important parameter in distributed geomorphologic and hydrologic modeling
A tool for the automatic aggregation and validation of the results of physically based distributed slope stability models
Full text linkDistributed physically based slope stability models usually provide outputs representing,
on a pixel basis, the probability of failure of each cell. This kind of result, although scientifically
sound, from an operational point of view has several limitations. First, the procedure of validation
lacks standards. As instance, it is not straightforward to decide above which percentage of failure
probability a pixel (or larger spatial units) should be considered unstable. Second, the validation
procedure is a time-consuming task, usually requiring a long series of GIS operations to overlap
landslide inventories and model outputs to extract statistically significant performance metrics.
Finally, if model outputs are conceived to be used in the operational management of landslide
hazard (e.g., early warning procedures), the pixeled probabilistic output is difficult to handle and
a synthesis to characterize the hazard scenario over larger spatial units is usually required to issue
warnings aimed at specific operational procedures. In this work, a tool is presented that automates
the validation procedure for physically based distributed probabilistic slope stability models and
translates the pixeled outputs in warnings released over larger spatial units like small watersheds.
The tool is named DTVT (double-threshold validation tool) because it defines a warning criterion
on the basis of two threshold values—the probability of failure above which a pixel should be
considered stable (failure probability threshold, FPT) and the percentage of unstable pixels needed
in each watershed to consider the hazard level widespread enough to justify the issuing of an alert
(instability diffusion threshold, IDT). A series of GIS operations were organized in a model builder to
reaggregate the raw instability maps from pixels to watershed; draw the warning maps; compare
them with an existing landslide inventory; build a contingency matrix counting true positives, true
negatives, false positive, and false negatives; and draw in a map the results of the validation. The
DTVT tool was tested in an alert zone of the Aosta Valley (northern Italy) to investigate the high
sensitivity of the results to the values selected for the two thresholds. Moreover, among 24 different
configurations tested, we performed a quantitative comparison to identify which criterion (in the
case of our study, there was an 85% or higher failure probability in 5% or more of the pixels of a
watershed) produces the most reliable validation results, thus appearing as the most promising
candidate to be used to issue alerts during civil protection warning activities
Elaborazione di un modello per la previsione dello spessore delle coperture superficiali nel bacino del Torrente Terzona
No full textAdvanced distributed modelling of slope stability using root reinforcement and geostatistical parameterization of geotechnical soil properties
Full text linkA physically based model for shallow landslide triggering (HIRESSS - HIgh REsolution Soil Stability Simulator) was applied in a 100 km2 test site in Central Italy (Urbino, Marche region). The objectives were assessing the influence of additional cohesion provided by roots and testing the effectiveness of a geotechnical characterization performed in an another area, but on similar lithologies.
We performed two different simulations considering the rainfall event of January-February 2006, which triggered 14 landslides in the area. For both the simulations, rainfall data were fed into the model using the measurements at hourly time step of a nearby rain gauge station, while soil thickness was estimated using a state-of-the-art empirical model based on geomorphological parameters derived from curvature, slope gradient, lithology and relative position within the hillslope profile. Geotechnical input data were varied among the two simulations. In the first one, a few in-situ and laboratory tests were performed to characterize the main lithologies, while the remaining lithologies were characterized using literature data. In the second simulation, the main geotechnical and hydrological parameters (cohesion, internal friction angle, soil unit weight, hydraulic conductivity) were fed into the model using a geostatistical characterization performed on hundreds of measurements carried out in another Italian region, with similar lithologies. Furthermore, in the second simulation the additional cohesion provided by the plant roots was also taken into account.
The results obtained with the two simulations were validated considering the landslide dataset collected by field work and image interpretation shortly after the rainfall event studied. We discovered that the second simulation provided much more reliable results, with the areas surrounding the landslide locations characterized by much higher values of failure probability.
The outcome is very important to address future research in distributed slope stability modelling because it proved that: (i) additional root cohesion is an important factor that can be used to get more reliable results; (ii) when in need of characterizing the geotechnical parameters of the study area, instead of using just a few measurements performed therein, it is preferable to integrate also data coming from different areas but with similar lithologies if they were robustly characterized in geostatistical terms purposely for distributed slope stability studies
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