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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
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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
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