1,721,200 research outputs found
Landslide susceptibility map refinement using PSInSAR data
Full text linkAbstractLandslide susceptibility maps (LSM) are commonly used by local authorities for land use management and planning activities, representing a valuable tool used to support decision makers in urban and infrastructural planning. The accuracy of a landslide susceptibility map is affected by false negative and false positive errors which can decrease the reliability of this useful product. In particular, false negative errors, are generally worse in terms of social and economic losses with respect to the losses associated with false positives. In this paper, we present a new technique to improve the accuracy of landslide susceptibility maps using Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) data. The proposed approach uses two different data sets acquired in ascending and descending geometry. The PS velocity measured along the line of sight is re-projected into a new velocity along the steepest slope direction (VSlope). Integration between the LSM and the ground deformation velocity map along the slope was performed using an empirical contingency matrix, which takes into account the average VSlope and the susceptibility degree obtained using the Random Forests algorithm. The Results show that the susceptibility degree increased in 56.41km2 of the study area. The combination of PSInSAR data and the landslide susceptibility map (LSM) improved the prediction reliability of slow moving landslides, which particularly affect urbanized areas. The use of this procedure can be easily applied in different areas where PSI data sets are available. This approach will help planning and decision-making authorities produce reliable landslide susceptibility maps, correcting some of the LSM errors
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
Detecting information from Twitter on landslide hazards in Italy using deep learning models
Full text linkAbstract Background Mass media are a new and important source of information for any natural disaster, mass emergency, pandemic, economic or political event, or extreme weather event affecting one or more communities in a country. Several techniques have been developed for data mining in social media for many natural events, but few of them have been applied to the automatic extraction of landslide events. In this study, Twitter has been investigated to detect data about landslide events in Italian-language. The main aim is to obtain an automatic text classification on the basis of information about natural hazards. The text classification for landslide events in Italian-language has still not been applied to detect this type of natural hazard. Results Over 13,000 data were extracted within Twitter considering five keywords referring to landslide events. The dataset was classified manually, providing a solid base for applying deep learning. The combination of BERT + CNN has been chosen for text classification and two different pre-processing approaches and bert-model have been applied. BERT-multicase + CNN without preprocessing archived the highest values of accuracy, equal to 96% and AUC of 0.96. Conclusions Two advantages resulted from this studio: the Italian-language classified dataset for landslide events fills that present gap of analysing natural events using Twitter. BERT + CNN was trained to detect this information and proved to be an excellent classifier for the Italian language for landslide events
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
Uncertainties of landslide susceptibility prediction: influences of different study area scales and mapping unit scales
Full text linkThis study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction (LSP). To illustrate various study area scales, Ganzhou City in China, its eastern region (Ganzhou East), and Ruijin County in Ganzhou East were chosen. Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m, as well as slope units that were extracted by multi-scale segmentation method. The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs. Then, landslide susceptibility maps (LSMs) of Ganzhou City, Ganzhou East and Ruijin County are produced using a support vector machine (SVM) and random forest (RF), respectively. The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City, along with the LSMs of Ruijin County from Ganzhou East. Additionally, LSMs of Ruijin at various mapping unit scales are generated in accordance. Accuracy and landslide susceptibility indexes (LSIs) distribution are used to express LSP uncertainties. The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City, Ganzhou East to Ruijin County, whereas those under slope units are less affected by study area scales. Of course, attentions should also be paid to the broader representativeness of large study areas. The LSP accuracy of slope units increases by about 6%-10% compared with those under grid units with 30 m and 60 m resolution in the same study area's scale. The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large. The importance of environmental factors varies greatly with the 60 m grid unit, but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.[GRAPHICS]
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