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Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment

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Abstract and Figures

Landslide susceptibility maps can be developed with artificial neural networks (ANNs). In order to train our ANNs, a digital elevation model (DEM) and a scar map of one previous event were used. Eleven attributes are generated, possibly containing redundant information. Our base model is formed by, essentially, one input (the DEM), eleven attributes, 30 neurons, and one output (susceptibility). Principal components (PCs) group information in the first projected variables, the last ones can be expendable. In the present paper, four groups of models were trained: one with eleven attributes generated from the DEM; one with 8 out of 11 attributes, in which 3 were eliminated by their high correlation with others; other, with the data projected over its PCs; and another, using 8 out of 11 PCs. The used number of neurons in hidden layer is 30, calibrated based on a complexity analysis that is an in-house developed method. The ANN models trained with the original data generated better statistical results than their counterparts trained with the PC transformed input. Keeping the original 11 attributes calculated provided the best metrics among all models, showing that eliminating attributes also eliminates information used by the model. Using 11 PC transformed attributes hindered trained. However, for the model with eight PCs, training was much faster than its counterpart with little accuracy loss. The metrics and maps achieved were considered acceptable, conveying the power of our model based on ANNs, which uses essentially one input (the DEM) for mapping areas susceptible to mass movements.
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/ Published online: 21January2020
Attribute selection using correlations and principal
components for artificial neural networks employment
for landslide susceptibility assessment
ısa Vieira Lucchese ·
Guilherme Garcia de Oliveira ·
Olavo Correa Pedrollo
Received: 12 May 2019 / Accepted: 11 November 2019
© Springer Nature Switzerland AG 2020
Abstract Landslide susceptibility maps can be devel-
oped with artificial neural networks (ANNs). In order
to train our ANNs, a digital elevation model (DEM)
and a scar map of one previous event were used.
Eleven attributes are generated, possibly containing
redundant information. Our base model is formed by,
essentially, one input (the DEM), eleven attributes,
30 neurons, and one output (susceptibility). Principal
components (PCs) group information in the first pro-
jected variables, the last ones can be expendable. In
the present paper, four groups of models were trained:
one with eleven attributes generated from the DEM;
one with 8 out of 11 attributes, in which 3 were elimi-
nated by their high correlation with others; other, with
the data projected over its PCs; and another, using 8
out of 11 PCs. The used number of neurons in hidden
layer is 30, calibrated based on a complexity analy-
sis that is an in-house developed method. The ANN
models trained with the original data generated bet-
ter statistical results than their counterparts trained
ısa Vieira Lucchese ()·Olavo Correa Pedrollo
Instituto de Pesquisas Hidr´
aulicas, Universidade Federal do
Rio Grande do Sul, Av. Bento Gonc¸alves,9500, Porto Ale-
gre, Brazil
Guilherme Garcia de Oliveira
Departamento Interdisciplinar, Universidade Federal do Rio
Grande do Sul, Rodovia RS 030, 11700, km 92. Emboaba,
ı, RS, 95590-000, Brazil
with the PC transformed input. Keeping the origi-
nal 11 attributes calculated provided the best metrics
among all models, showing that eliminating attributes
also eliminates information used by the model. Using
11 PC transformed attributes hindered trained. How-
ever, for the model with eight PCs, training was much
faster than its counterpart with little accuracy loss. The
metrics and maps achieved were considered accept-
able, conveying the power of our model based on
ANNs, which uses essentially one input (the DEM) for
mapping areas susceptible to mass movements.
Keywords Landslide ·Multilayer perceptron ·
Dimensionality reduction ·Susceptibility map
Landslides are natural hazards that occur when a mass
of soil detaches from its place and slides down a
slope (Cruden 1991), possibly causing damage to lives
and properties. With worldwide population growth,
human occupation of hazardous areas has substan-
tially increased over the past decades, and the impact
of natural disasters has been largely magnified in both
industrialized and developing countries (Guzzetti et al.
1999). Between 1971 and 1974, nearly 600 people per
year were killed by landslides (Schuster and Fleming
1986). The fatality rate has increased to 4617 people
per year between 2014 and 2010, during which 32,322
Environmental Monitoring and Assessment (2020) 192: 129
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... Among the most used are logistic regression (Yilmaz, 2009;Hong et al., 2015;Bui et al., 2016), weights-of-evidence (Pourghasemi et al., 2012;Hong et al., 2018a), and statistical index (Regmi et al., 2014;Aghdam et al., 2016). In recent years, machine learning (ML) techniques have stood out in creating landslide susceptibility models, with emphasis on artificial neural networks (Chen et al., 2017a;Oliveira et al., 2019a;Lucchese et al., 2020;Wang et al., 2020, Jennifer & Saravanan (2021, decision trees (Oliveira et al., 2019a;Sachdeva et al., 2019), support vector machines (Sachdeva et al., 2019;Wang et al., 2020) and ensemble modelling, like Generalized Boosting Model and Maximum Entropy (Novellino et al., 2021;Di Napoli et al., 2021). These models work by extracting knowledge from previously selected samples and, first, go through a training phase through explanatory variables, which must be carefully selected to generate consistent training and to use all the learning capacity that the technique allows. ...
... After creating these attributes, many studies perform an analysis to find out how they relate to the model's input variables (samples of occurrence and non-occurrence), using methods such as Pearson correlation (Bui et al., 2016;Wang et al., 2020;Lucchese et al., 2020). To hasten and improve the modeling process, Pearson A c c e p t e d M a n u s c r i p t correlation analysis was used to identify the attributes least correlated with the occurrence and non-occurrence samples and exclude them from the modeling process. ...
... To avoid overfitting the model, a series of cross-validation was used along with the training series. As in studies of Oliveira et al. (2019a) and Lucchese et al. (2020), the proportion of samples is 50% / 25% / 25%, for training, cross-validation, and testing, respectively. The training and cross-validation process was carried out in two steps. ...
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Landslides can have serious environmental, economic, and social consequences. Using artificial neural networks (ANN) to map these landslides is becoming more frequent every year, being one of the most reliable methods for this. Among the prime influences on the generated maps, sample areas are significantly interesting, since they directly influence the result. In this research, we investigated how the performance of these models is influenced by the use of partial sampling (with landslides caused by a single precipitation event - Single Model) or total (with landslides caused by multiple precipitation events - Full Model). This is one of the main topics that our study approaches. This study aims to evaluate the criteria for landslide sampling and ANN modeling by analyzing the influence of distance on the sampling processes, the use of multiple landslide events, and the relationship between terrain attributes and susceptibility models. To this end, were used five sampling areas (1638 points samples of landslides) in the Serra Geral, southern Brazil, distance buffers in the non-occurrence sampling process (2-40 km), and 16 terrain attributes. The training of the multilayer network was carried out by backpropagation algorithm, and the accuracy was calculated using the Area Under the Receiver Operating Characteristic Curve. The results showed that the greater the distances of the non-occurring samples, the greater the accuracy of the model, with a 40 km buffer resulting in the best models. They also showed that the use of multiple events (Full Model) produced better results than each event used separately (Single Model), obtaining accuracies of 0.954 and 0.931, respectively. This is mainly because there is greater differentiation between occurrence and non-occurrence samples when using multiple events, thus facilitating the distinction between more and less susceptible areas.
... Quantitative techniques are based on statistical models to generate LSM (Guzzetti et al. 2000;Gong et al. 2018). Several researchers have developed various methods for landslide susceptibility considering landslide conditioning and triggering parameters (Zhu et al. 2014;Wu et al. 2016;Pourghasemi and Rahmati 2018;Roccati et al. 2019;Moharrami et al. 2020;Tyagi et al. 2022), including frequency ratio (Suzen and Doyuran 2004;Pal and Chowdhuri 2019), logistic regression analysis (Dai and Lee 2002;Ayalew and Yamagishi 2005;Lee and Pradhan 2007;Nefeslioglu et al. 2008;Nandi and Shakoor 2010;Bai et al. 2011;Kavzoglu et al. 2015;Erener et al. 2016;Tsangaratos and Ilia 2016;Zhu et al. 2018;Zhang et al. 2019;Zhao et al. 2019;Du et al. 2020;Goyes-Peñafiel and Hernandez-Rojas 2021;Orhan et al. 2022), artificial neural network analysis (Lee et al. 2001;Gomez and Kavzoglu 2005;Kanungo et al. 2006;Lee and Pradhan 2007;Arnone et al. 2014;Gelisli et al. 2015;Tien Bui et al. 2016;Lucchese et al. 2020;Phong et al. 2021), fuzzy logic (Juang et al. 1992;Ercanoglu and Gokceoglu 2004;Pradhan et al. 2009;Akgun et al. 2012;Dehnavi et al. 2015;Uvaraj and Neelakantan 2018;Razifard et al. 2019;Baharvand et al. 2020;Ozer et al. 2020), and random forest (Youssef et al. 2016;Pourghasemi and Kerle 2016;Chen et al. 2017;Kim et al. 2018;Taalab et al. 2018;Hong et al. 2019;Chu et al. 2019;Dou et al. 2019;Sevgen et al. 2019;Dang et al. 2020;Zhao et al. 2021;Orhan et al. 2022). Machine learning and artificial intelligence have become increasingly popular for the analysis of spatial estimation in natural disaster studies, involving artificial neural networks, random forest, and fuzzy logic (Das and Lepcha 2019;Shirvani 2020). ...
Landslides often cause significant economic and human losses, and therefore landslide susceptibility mapping (LSM) has become increasingly important. Accurate assessment of LSM is important for appropriate land use management and risk assessment. The aim of this study is to define and compare the results of applying the random forest (RF) and logistic regression (LR) models for estimating landslide susceptibility, and also to confirm the accuracy of the resulting susceptibility maps in the Ordu-Bolaman River micro-basin. The study area was selected because it is one of the most landslide-prone areas in Türkiye. First, a total of 231 landslide locations were identified. Then 12 landslide-influencing factors were selected to generate landslide susceptibility maps. These maps were produced using the landslide influencing factors based on the RF and LR models in a geographical information system (GIS) environment. Finally, area under the curve (AUC) analysis, sensitivity, specificity, and accuracy were considered to assess and compare the performance of the two models. In addition, the maps were retested with large landslides not included in the training and test data sets, using general accuracy criteria. The results of the present study will be helpful for future landslide risk mitigation efforts in the research area. Practical Applications: Landslide susceptibility mapping is crucial in adequately mitigating hazards and provides guidelines for landslide-prone areas to avoid hazards in the future. Therefore, landslide susceptibility assessment is of the utmost significance to ensure the safety of human life, mitigate the negative impacts on the economy, and prevent landslide hazards. Government agencies, policymakers, local authorities , and urban planners can use landslide susceptibility maps to plan effective management strategies for landslide prevention and mitigation and to make informed decisions regarding land use zoning and development. By identifying areas with very high and high landslide susceptibility , constructing critical infrastructure and buildings in hazardous zones can be avoided, reducing the risk of damage and loss during potential landslide events. Furthermore, landslide susceptibility maps play a crucial role in disaster risk management and emergency response planning. Authorities can use these maps to prioritize areas for early warning systems, evacuation routes, and disaster response teams. Farmers and landowners can benefit from the maps by being made aware of landslide-prone areas on their property. In addition, insurance companies can use landslide susceptibility maps to assess the risk of landslides in certain regions and adjust their insurance policies accordingly. By implementing these practical applications, landslide susceptibility maps can have a significant impact on reducing the vulnerability of communities and infrastructure to landslides, ultimately contributing to safer and more resilient environments. Consequently, this study is an example of landslide susceptibility mapping efforts for an agriculturally important area.
... Various conventional methods like the information value method, Fuzzy logic, weights of evidence model, modified information value method, weighted overlay, Frequency ratio, logistic regression etc., are popular among researchers (Batar and Watanabe, 2021;Luo, X et al., 2019;Akgun, 2012;Farooq, S., & Akram, M. S. 2021;Alsabhan et al., 2022;Sarda and Pandey, 2019;Hutchinson, 1988;Vijith et al., 2009, Zêzere, 2002Wang and Sassa, 2005;Akbar and Ha, 2011;Pereira et al., 2012;Lee andTalib, 2005, Abdullah H.Alsabhan et al., 2022;Avinash and Ashamanjari, 2010;Li L. et al., 2022;Kumar et al., 2018;Sonker et al., 2021, Sonker andTripathi, 2022). Additionally, new models are constantly developed and tested (Xiao et al., 2019;Dou et al., 2019;Peethambaran et al., 2019;Lucchese et al., 2020;Ado et al., 2022;Ullah et al., 2022). ...
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The Mountainous terrain,in the Himalayas is experiencing rapid development in a bewildering manner, which makes it more susceptible to landslides. Management and mitigation of landslide hazard begin with its mapping by integrating numerous methods and Geographic Information System (GIS) tools. However, it is difficult to produce reliable landslide susceptibility maps (LSM) with traditional remote sensing and GIS methods due to complexity of the mountainous terrain environments and huge datasets. Therefore, the present study investigates the applicability of Mamdani’s fuzzy inference system (FIS) to produce LSM in Himalayan terrain in India. It is compared with commonly used frequency ratio (FR) and information value method (IVM) approaches. Several causative factors were extracted and used to prepare thematic layers, including slope, aspect, curvature, solar radiance, SPI, TWI, rainfall, soil depth and NDVI. Landslide inventory was also created using google earth images and previously published work. The accuracy estimates for FR, IVM and FIS were performed based on ROC curves. FIS was found to provide an accuracy of 77.7%, followed by IVM (72%) and FR (71%) for LSM. The current study is a prototype for further studies in the Garhwal Himalayas and similar terrains, based on the vigorous Mamdani’s techniques of fuzzy inference theory. The outcomes of this work propose that an expert’s knowledge-based FIS method can produce an accurate LSM in such a complex terrain. Planners and concerned authorities can use the results further for landslide management and mitigation.
... In recent years, with the development of remote sensing and GIS technology, researchers all over the world have widely applied remote sensing and GIS technology to the field of landslide risk research, including the analytic hierarchy process (Achu and Reghunath 2017;Guo et al. 2014;Hou et al. 2006;Pourghasemi et al. 2012), the frequency ratio method (Lee and Sambath 2006;Yilmaz 2009), the weight of evidence method (Cao et al. 2021;Hong et al. 2017;Kayastha et al. 2012), the information value method (Che et al. 2012;Farooq and Akram 2021;Guo-Liang et al. 2017), and the artificial neural networks (Lucchese et al. 2021;Lucchese et al. 2020;Soma et al. 2019). These spatio-temporal analysis methods allow us to analyse the probability of landslides qualitatively or quantitatively and to produce maps of landslide risk distribution (Tong et al. 2021). ...
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The traditional landslide risk research mostly focuses on the spatial distribution of landslide occurrence probability, but the research on the time distribution of landslide occurrence probability is not in-depth, and there is no effective professional management advice for specific risk areas, so it has great limitations. This paper takes the landslide-prone area along the Qingjiang River in Jianshi County, Hubei Province as the research area, takes the slope as the unit, and uses the information value method to complete the landslide geological disaster risk assessment in the study area. Through the characteristics of landslide risk in time and space, the dynamic management measures of landslide risk in different periods of the study area are formulated. The map of landslide prevention and control planning measures in different periods in the study area can be obtained, which provides a basis for realizing the balance between safety and economy of landslide prevention and control planning. This has obvious guiding significance for landslide prevention and control planning in other regions.
... Previous studies adopted various statistical measures and methods to evaluate the performance of a model. For instance, Petschko et al. (2014), Pineda et al. (2016), and Rodrigues et al. (2021) employed the area under the receiver operating characteristic curve (AUROC) to assess model's predictive ability; Lin et al. (2019), Tanyas et al. (2019), and Lucchese et al. (2020) used the proportion of true positives and true negatives among the total number of samples examined in a confusion matrix, namely the overall accuracy (ACC) of models trained and validated by different inventories to assess the model performance; Chung and Fabbri (2008), Ozioko and Igwe (2020), and Tien Bui et al. (2020) used the area under the success rate curve (AUSRC) to evaluate the predictive ability. Moreover, Knevels et al. (2020) and Lei et al. (2020) performed the Wilcoxon signedrank test to compare performances of susceptibility models constructed by various methods. ...
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When performing a landslide susceptibility analysis, a model is usually established on the basis of a multi-temporal or event-triggered landslide inventory. Because multi-temporal landslide inventories for most areas are rarely available, an event-triggered landslide inventory is often used, but the result depends on the selection of single event. In order to establish a landslide susceptibility model with a good prediction performance, the present study tried to find out how to select a single event-triggered landslide inventory, and investigated the effect of various combinations of event inventories. We selected Shihmen reservoir watershed as the research area, conducted a logistic regression analysis to build 23 event-based landslide susceptibility models and one multi-year landslide susceptibility model, and estimated the performance of these models. In addition, this study further assessed the influence of event characteristics on the model prediction performance, used the above results to merge two different events, and then established models based on these combinations. The results indicated that when establishing an event-based landslide susceptibility model, selecting events with suitable rainfall return periods and landslide density can yield robust models with relatively high predictive ability. Furthermore, the combination of two events which negatively correlate with each other in rainfall spatial distributions can enhance a model’s predictive ability and modeling efficiency.
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Landslide susceptibility mapping (LSM) is the foundation and critical part of landslide risk assessment. The bibliometric analysis of LSM literature can be applied to quantitatively analyze the research progress and development trend. The result will provide references for geological hazard risk assessment in China. In this study, based on the Web of Science and CNKI databases, the CiteSpace visual knowledge graph analysis tool has been used to carry out bibliometric analysis of LSM literature from 1985 to 2022. Moreover, the LDA analysis has been conducted on the abstract to subdivide the research in this field. The results showed that: (1) LSM is still a research hotspot at present. In China, there are a large number of studies and international cooperation about LSM. (2) Four of the top 10 authors in the number of published papers on LSM are from China. The institution that published the most papers on LSM is the Chinese Academy. The Chinese Journal of Geological Hazard and Control is the most popular Chinese journal and the Natural Hazards is the most popular English journals to publish LSM papers. The research on the subject of LSM has been greatly funded by the National Natural Science Foundation of China and the National Land and Resources Survey Project. (3) In the past five years, machine learning models (including deep learning, etc.) have been widely used as the most popular LSM models. (4) In order to achieve the simplification and intelligence of landslide susceptibility modeling and to improve the accuracy and practicability of the LSM results, the following parts of LSM, including the landslide inventory, conditioning factors, assessment unit, assessment model, connection methods and accuracy verification, need to be deeply explored in further studies.
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Landslide susceptibility prediction has always been an important and challenging content. However, there are some uncertain problems to be solved in susceptibility modeling, such as the error of landslide samples and the complex nonlinear relationship between environmental factors. A self-screening graph convolutional network and long short-term memory network (SGCN-LSTM) is proposed int this paper to overcome the above problems in landslide susceptibility prediction. The SGCN-LSTM model has the advantages of wide width and good learning ability. The landslide samples with large errors outside the set threshold interval are eliminated by self-screening network, and the nonlinear relationship between environmental factors can be extracted from both spatial nodes and time series, so as to better simulate the nonlinear relationship between environmental factors. The SGCN-LSTM model was applied to landslide susceptibility prediction in Anyuan County, Jiangxi Province, China, and compared with Cascade-parallel Long Short-Term Memory and Conditional Random Fields (CPLSTM-CRF), Random Forest (RF), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD) and Logistic Regression (LR) models.The landslide prediction experiment in Anyuan County showed that the total accuracy and AUC of SGCN-LSTM model were the highest among the six models, and the total accuracy reached 92.38 %, which was 5.88%, 12.44%, 19.65%, 19.92% and 20.34% higher than those of CPLSTM-CRF, RF, SVM, SGD and LR models, respectively. The AUC value reached 0.9782, which was 0.0305,0.0532,0.1875,0.1909 and 0.1829 higher than the other five models, respectively. In conclusion, compared with some existing traditional machine learning, the SGCN-LSTM model proposed in this paper has higher landslide prediction accuracy and better robustness, and has a good application prospect in the LSP field.
In this paper, an original methodology for landslide susceptibility mapping (LSM) is presented. It consists of bagging ensembles of artificial neural networks (ANNs) and random forests (RFs), and hybrid bagging ensembles of these models. It is applied on the area of the Itajaí-Açu river valley. In December 2020, there was an extreme rainfall in the region, which triggered landslides. The RF ensemble presented slightly higher accuracy (0.941) than the ANN ensemble (0.940), but the ANN ensemble had a more balanced relation between sensitivity (0.966) and specificity (0.915) than the RF ensemble (specificity = 0.992, sensitivity = 0.891). The mixed ANN-RF ensemble presented the higher accuracy of all (0.950), and a good balance between sensitivity (0.948) and specificity (0.951), being considered the best model within those analyzed. The hybrid ensemble, together with classification threshold adjustment, removed discrepancies on the maps between both models by attenuating them.
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We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), and random subspace (RS), were used to construct new ensemble models. SVM has been used previously to spatially predict landslides, but not together with its ensembles. We selected 20 conditioning factors and randomly portioned 98 landslide locations into training (70%) and validating (30%) groups. Several statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC), were used for model comparison and validation. Using the One-R Attribute Evaluation (ORAE) technique, we found that all 20 conditioning factors were significant in identifying landslide locations, but "distance to road" was found to be the most important. The RS (AUC = 0.837) and RF (AUC = 0.834) significantly improved the goodness-of-fit and prediction accuracy of the SVM (AUC = 0.810), whereas the BA (AUC = 0.807) and AB (AUC = 0.779) did not. The random subspace based support vector machine (RSSVM) model is a promising technique for helping to better manage land in landslide-prone areas.
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Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes-and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-Remote Sens. 2019, 11, 638 2 of 30 prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides.
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The aim of this paper was to identify and analyze the susceptible areas to debris flow in the Taquari-Antas River basin. We developed a spatial modeling with probabilistic approach involving the morphometric analysis in areas with occurrence of debris flow for the mapping of susceptible areas. The sites were inventoried from satellite images and on-site expeditions, have been mapped 193 scars. Most scars refer to the event occurred in January 2010, in the Forqueta river basin. We deϐined three morphometric attributes for modeling: (i) the average slope ϐiltered in 5x5 window; (ii) altimetry slope of the ramp; (iii) altimetry slope of the hill. These attributes showed a well-deϐined central tendency, low data dispersion and low correlation with each other. The mapped scars of landslides have a total area of 27.3 ha, most of them with a length of more than 150 m and a width of around 10 m. The average altimetric slope of the hills with mass movements was 317 m, with a mean slope of 39%. The results indicate that the susceptible areas to debris ϐlow, 8.147 km² (30% of the basin), principally are located along the erosive escarpment lines, in contact between the Serra Geral and the adjacent geomorphological units. The lines of escarpment erosive are located on the slopes of the das Antas, da Prata, São Marcos, Carreiro, Guaporé, Forqueta, Fão and Taquari river valleys. In absolute terms, the municipalities with most susceptible areas that are Bom Jesus, Jaquirana and Fontoura Xavier. About 40 municipalities present more than 50% of their areas as susceptible to debris flows.
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In the present study, Rotation Forest ensemble was integrated with different base classifiers to develop different hybrid models namely Rotation Forest based Support Vector Machines (RFSVM), Rotation Forest based Artificial Neural Networks (RFANN), Rotation Forest based Decision Trees (RFDT), and Rotation Forest based Naïve Bayes (RFNB) for landslide susceptibility modelling. The validity of these models was evaluated using statistical methods such as Root Mean Square Error (RMSE), Kappa index, accuracy, and the area under the success rate and predictive rate curves (AUC). Part of the landslide prone area of Pithoragarh district, Uttarakhand, Himalaya, India was selected as the study area. Results indicate that the RFDT is the best model showing the highest predictive capability (AUC =0.741) in comparison to RFANN (AUC =0.710), RFSVM (AUC =0.701), and RFNB (AUC =0.640) models. The present study would be helpful in the selection of best model for landslide susceptibility mapping.
Landslide susceptibility mapping is vital for landslide risk management and urban planning. In this study, we used three statistical models [frequency ratio, certainty factor and index of entropy (IOE)] and a machine learning model [random forest (RF)] for landslide susceptibility mapping in Wanzhou County, China. First, a landslide inventory map was prepared using earlier geotechnical investigation reports, aerial images, and field surveys. Then, the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis. To determine the most effective causal factors, landslide susceptibility evaluations were performed based on four cases with different combinations of factors (“cases”). In the analysis, 465 (70%) landslide locations were randomly selected for model training, and 200 (30%) landslide locations were selected for verification. The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model. Finally, the receiver operating characteristic (ROC) curve was used to verify the accuracy of each model’s results for its respective optimal case. The ROC curve analysis showed that the machine learning model performed better than the other three models, and among the three statistical models, the IOE model with weight coefficients was superior.
Landslides represent a part of the cascade of geological hazards in a wide range of geo-environments. In this study, we aim to investigate and compare the performance of two state-of-the-art machine learning models, i.e., decision tree (DT) and random forest (RF) approaches to model the massive rainfall-triggered landslide occurrences in the Izu-Oshima Volcanic Island, Japan at a regional scale. At first, a landslide inventory map is prepared consisting of 44 landslide polygons (10,444 pixels) from aerial photo-interpretation and field surveys. To estimate the robustness of the models, we randomly adapted two different samples (S1 and S2), comprising of both positive and negative cells (70% of total landslides - 7293 pixels) for training and remaining (30%–3151 pixels) for validation. Twelve causative factors including altitude, slope angle, slope aspect, plan curvature, total curvature, compound topographic index, stream power index, distance to drainage network, drainage density, distance to geological boundaries, lithology and cumulative rainfall were selected as predictors to implement the landslide susceptibility model. The area under the receiver operating characteristics (ROC) curves (AUC) and other statistical signifiers were used to verify the model accuracies. The result shows that the DT and RF models achieved remarkable predictive performance (AUC > 0.9), producing near accurate susceptibility maps. The overall efficiency of RF (AUC = 0.956) is found significantly higher than the DT (AUC = 0.928) results. Additionally, we noticed that the performance of RF for modeling landslide susceptibility is very robust even though the training and validation samples are altered. Considering the performances, we suggest that both RF and DT models can be used in other similar non-eruption-related landslide studies in the tephra-deposited rich volcanoes, as they are capable of rapidly generating accurate and stable LSM maps for risk mitigation, management practices, and decision-making. Moreover, the RF-based model is promising and enough to be recommended as a method to map regional landslide susceptibility.
The present study is dealt with the preparation of landslide susceptibility map of Darjeeling Himalaya with the help of GIS tools and artificial neural network (ANN) model. Fifteen landslide causative factors, i.e. elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized differential vegetation index (NDVI) and land use and land cover (LULC) were considered to produce the landslide susceptibility zonation map. To generate all these aforesaid causative factors map, topographical maps, geological map, soil map, satellite imageries, Google earth images and some other authorized maps were processed and constructed into a spatial data base using GIS and image processing techniques. The back-propagation method was applied to estimate factor’s weight and the landslide hazard indices were derived with the help of trained back-propagation weights. Then, the landslide susceptibility zonation map of Darjeeling Himalaya was made using GIS tool and classified into five, i.e. very low, low, moderate, high, and very low landslide susceptibility. To validate the prepared landslide susceptibility map, landslide inventory was used and accuracy result was obtained after processing ROC curve. The accuracy of the landslide susceptibility map was 81.5% which is desirable.
Statistically based landslide susceptibility mapping has become an important research area in the last decades, and several bivariate and multivariate statistical approaches to landslide susceptibility assessments have been applied and compared in all regions of the world. The aim of this study was to compare different statistical approaches and to analyse the degree of spatial agreement between the landslide susceptibility maps produced. To this end, we selected seven statistical methods for comparison, namely, landslide density, likelihood ratio, information value, Bayesian model, weights of evidence, logistic regression and discriminant analysis, and then applied these to an inventory comprising 940 translational landslides, in the southeast region of Minas Gerais state in Brazil, at the western edge of the Quadrilátero Ferrífero (642.13 km²). In some statistical approaches, modifications were made to the input dependent variables. The landslides registered in the inventory map have been used in punctual and polygonal form. Six factors were considered as input landslide predisposing factors: slope angle, geomorphological units, slope curvature, lithological units, slope aspect and inverse wetness index. The combination order of the landslide predisposing factors was established based on a sensitivity analysis, which gave rise to five different cartographic combinations. In total, 58 statistical models of landslide susceptibility were produced, and the results were validated using success and prediction rate curves. The spatial agreement evaluation between the model results was carried out with kappa statistics. There were 214 comparisons of spatial agreement involving classified models at three relative degrees of susceptibility (high, medium and low landslide susceptibility classes). The results showed that all of the models so produced had satisfactory validation rates. The best landslide susceptibility models obtained areas under the curve of > 0.80 in the success and prediction rate curves, with emphasis on the weights of evidence, the information value and the likelihood ratio statistical methods. These statistical approaches were performed with the landslides mapped in the form of points. The landslide susceptibility classes of these models visually demonstrated a slightly more irregular spatial distribution when compared to the models performed with landslide polygons. The likelihood ratio model performed with landslide points presented one of the smallest areas for the high susceptibility class and the largest area for the low susceptibility class. The analysis of the spatial agreement showed that the models produced with a polygonal dependent variable tend to be more concordant, regardless of the statistical technique used. Moreover, we verified that spatial agreement tends to increase with increasing accuracy of the models. Despite the discrepancies found, most of the models compared showed a substantial or almost perfect degree of agreement.