An attempt to increase the geological information in landslide susceptibility mapping and sensitivity to different geological parameters

2020 ◽  
Author(s):  
Tania Luti ◽  
Samuele Segoni ◽  
Bimla Tamburini ◽  
Giulio Pappafico ◽  
Filippo Catani
Keyword(s):  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
Input Parameter ◽  
Test Site ◽  
Landslide Susceptibility Mapping ◽  
Significant Information ◽  
Geological Maps ◽  
Lithostratigraphic Units ◽  
Geological Information ◽  
Geological Map

<p>Geological 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.</p><p>Our test site is a 3100 km<sup>2</sup> 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).</p><p>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.</p><p>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.</p><p>These outcomes can to assist future landslide susceptibility studies for different reasons:</p><p>(i)at least in our study area, lithology, which is commonly used in LS, did not provide the best results;</p><p>(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).</p><p>(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.</p>

scholarly journals Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization

Landslides ◽  
2020 ◽  
Vol 17 (10) ◽  
pp. 2443-2453 ◽  
Author(s):  
Samuele Segoni ◽  
Giulio Pappafico ◽  
Tania Luti ◽  
Filippo Catani
Keyword(s):  
Landslide Susceptibility ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Assessment ◽  
Geological Maps ◽  
Geological Information ◽  
Susceptibility Model ◽  
Validation Procedure ◽  
Geological Map

AbstractThe 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.

scholarly journals Precambrian metamorphites as part of the petrographic diversity of Rila Geopark

2021 ◽  
Vol 82 (1) ◽  
pp. 11-29
Author(s):  
Dimitar Sinnyovsky
Keyword(s):  
Popular Science ◽  
Metamorphic Rocks ◽  
Historical Overview ◽  
Geological Processes ◽  
Lithostratigraphic Units ◽  
Intrusive Rocks ◽  
Geological Information ◽  
Geological Map ◽  
The Republic ◽  
Popular Language

The petrographic diversity of Rila Mountain is an important prerequisite for the development of Rila Geopark and its nomination for UNESCO Geopark. According to the principle of the thematic geodiversity, the leading theme of the geopark – glacial landscapes, is complemented by the remarkable petrographic diversity of igneous and metamorphic rocks, in which alpine glacial forms are developed. Following a recent review of the intrusive rocks that make up the Rila-West Rhodope Batholith, the concept of Rila Geopark would not be complete without a retrospective of the metamorphic rocks that are an integral part of its petrographic diversity. The different approaches (lithodemic and lithotectonic) to the mapping of the Rila-Rhodope Massif divided the geologists into two camps. This led to a discrepancy in terminology and nomenclature of the metamorphic units, which is unacceptable in the context of the educational priority of the geopark. For the purpose of geotourism, clear and understandable interpretations are required to provoke the visitors’ interest in geological processes and phenomena, rather than confusion and perplexity. This article is a historical overview of the geological study of the metamorphic terrains in Rila Mountain with a scheme of lithodemic units based on the existing lithostratigraphic nomenclature. This approach allows the preservation of the names of the lithostratigraphic units, whose toponymic adjectives derive from well known geographical features in the Rila-Rhodope Massif. The rank term is replaced by a lithological or descriptive term, which frees the nomenclature from the dogmatic restrictions of the stratigraphic code and reduces the distance between the strictly scientific and popular science approach used for interpretation of geological information in a popular language accessible to the general public. This methodology is consistent with the approach recommended for mapping of non-stratified bodies on the Geological Map of the Republic of Bulgaria at a scale 1:50 000.

scholarly journals Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1737 ◽  
Author(s):  
Bahareh Kalantar ◽  
Naonori Ueda ◽  
Vahideh Saeidi ◽  
Kourosh Ahmadi ◽  
Alfian Abdul Halin ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
Computational Modelling ◽  
Susceptibility Mapping ◽  
Boosted Regression Trees ◽  
Supervised Machine Learning ◽  
Landslide Susceptibility Mapping ◽  
Ensemble Model ◽  
Conditioning Factors

Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the identification of landslide prone areas. Computational modelling techniques have been successful in related disaster scenarios, which motivate this work to explore such modelling for LSM. In this research, the potential of supervised machine learning and ensemble learning is investigated. Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvements. The dataset used to train and test all the algorithms consists of a landslide inventory map of 227 landslide locations. From these sources, 13 conditioning factors are extracted to be used in the models. Experimental evaluations are made based on True Skill Statistic (TSS), the Receiver Operation characteristic (ROC) curve and kappa index. The results show that the best TSS (0.6986), ROC (0.904) and kappa (0.6915) were obtained by the ensemble model. FDA on its own seems effective at modelling landslide susceptibility from multiple data sources, with performance comparable to GLM. However, it slightly underperforms when compared to GBM (BRT) and RF. RF seems most capable compared to GBM, GLM, and FDA, when dealing with all conditioning factors.

scholarly journals A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shuai Zhao ◽  
Zhou Zhao
Keyword(s):  
Support Vector Machine ◽  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Support Vector

The main purpose of this study aims to apply and compare the rationality of landslide susceptibility maps using support vector machine (SVM) and particle swarm optimization coupled with support vector machine (PSO-SVM) models in Lueyang County, China, enhance the connection with the natural terrain, and analyze the application of grid units and slope units. A total of 186 landslide locations were identified by earlier reports and field surveys. The landslide inventory was randomly divided into two parts: 70% for training dataset and 30% for validation dataset. Based on the multisource data and geological environment, 16 landslide conditioning factors were selected, including control factors and triggering factors (i.e., altitude, slope angle, slope aspect, plan curvature, profile curvature, SPI, TPI, TRI, lithology, distance to faults, TWI, distance to rivers, NDVI, distance to roads, land use, and rainfall). The susceptibility between each conditioning factor and landslide was deduced using a certainty factor model. Subsequently, combined with grid units and slope units, the landslide susceptibility models were carried out by using SVM and PSO-SVM methods. The precision capability of the landslide susceptibility mapping produced by different models and units was verified through a receiver operating characteristic (ROC) curve. The results showed that the PSO-SVM model based on slope units had the best performance in landslide susceptibility mapping, and the area under the curve (AUC) values of training and validation datasets are 0.945 and 0.9245, respectively. Hence, the machine learning algorithm coupled with slope units can be considered a reliable and effective technique in landslide susceptibility mapping.

scholarly journals An Experimental Research on the Use of Recurrent Neural Networks in Landslide Susceptibility Mapping

2019 ◽  
Vol 8 (12) ◽  
pp. 578 ◽  
Author(s):  
Begum Mutlu ◽  
Hakan A. Nefeslioglu ◽  
Ebru A. Sezer ◽  
M. Ali Akcayol ◽  
Candan Gokceoglu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
Landslide Susceptibility Mapping ◽  
Learning Approaches ◽  
Characteristic Analysis ◽  
Initiation Point ◽  
Landslide Mapping ◽  
The Individual

Natural hazards have a great number of influencing factors. Machine-learning approaches have been employed to understand the individual and joint relations of these factors. However, it is a challenging process for a machine learning algorithm to learn the relations of a large parameter space. In this circumstance, the success of the model is highly dependent on the applied parameter reduction procedure. As a state-of-the-art neural network model, representative learning assumes full responsibility of learning from feature extraction to prediction. In this study, a representative learning technique, recurrent neural network (RNN), was applied to a natural hazard problem. To that end, it aimed to assess the landslide problem by two objectives: Landslide susceptibility and inventory. Regarding the first objective, an empirical study was performed to explore the most convenient parameter set. In landslide inventory studies, the capability of the implemented RNN on predicting the subsequent landslides based on the events before a certain time was investigated respecting the resulting parameter set of the first objective. To evaluate the behavior of implemented neural models, receiver operating characteristic analysis was performed. Precision, recall, f-measure, and accuracy values were additionally measured by changing the classification threshold. Here, it was proposed that recall metric be utilized for an evaluation of landslide mapping. Results showed that the implemented RNN achieves a high estimation capability for landslide susceptibility. By increasing the network complexity, the model started to predict the exact label of the corresponding landslide initiation point instead of estimating the susceptibility level.

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