scholarly journals Optimization of Computational Intelligence Models for Landslide Susceptibility Evaluation

2020 ◽  
Vol 12 (14) ◽  
pp. 2180 ◽  
Author(s):  
Xia Zhao ◽  
Wei Chen
Keyword(s):  
Roc Curve ◽  
Landslide Susceptibility ◽  
Belief Function ◽  
Prediction Ability ◽  
Landslide Occurrence ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Intelligence Models ◽  
Logistic Model Tree ◽  
Susceptibility Evaluation

This paper focuses on landslide susceptibility prediction in Nanchuan, a high-risk landslide disaster area. The evidential belief function (EBF)-based function tree (FT), logistic regression (LR), and logistic model tree (LMT) were applied to Nanchuan District, China. Firstly, an inventory with 298 landslides was compiled and separated into two parts (70%: 209; 30%: 89) as training and validation datasets. Then, based on the EBF method, the Bel values of 16 conditioning factors related to landslide occurrence were calculated, and these Bel values were used as input data for building other models. The receiver operating characteristic (ROC) curve and the values of the area under the ROC curve (AUC) were used to evaluate and compare the prediction ability of the four models. All the models achieved good results and performed well. In particular, the LMT model had the best performance (0.847 and 0.765, obtained from the training and validation datasets, respectively). This paper also demonstrates the superiority of integration and optimization of models in landslide susceptibility evaluation. Finally, the best classification method was selected to draw landslide susceptibility maps, which may be helpful for government administrators and engineers to carry out land design and planning.

scholarly journals GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

2019 ◽  
Vol 11 (24) ◽  
pp. 7118 ◽  
Author(s):  
Viet-Tien Nguyen ◽  
Trong Hien Tran ◽  
Ngoc Anh Ha ◽  
Van Liem Ngo ◽  
Al-Ansari Nadhir ◽  
...  
Keyword(s):  
Land Use ◽  
Risk Management ◽  
Land Use Planning ◽  
Computational Intelligence ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Statistical Measures ◽  
Susceptibility Maps ◽  
Intelligence Models ◽  
Landslide Susceptibility Maps

Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

scholarly journals Assessing the importance of feature selection in Landslide Susceptibility for Belluno province (Veneto Region, NE Italy)

2021 ◽  
Author(s):  
Sansar Raj Meena ◽  
Silvia Puliero ◽  
Kushanav Bhuyan ◽  
Mario Floris ◽  
Filippo Catani
Keyword(s):  
Landslide Susceptibility ◽  
Regional Scale ◽  
Belief Function ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk ◽  
Veneto Region ◽  
Conditioning Factors ◽  
Topographic Features ◽  
Ne Italy ◽  
Trial And Error Method

Abstract. In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (Frequency Ratio and Evidence Belief Function) and two machine learning (ML) models (Random Forest and XG-Boost) for LSM in the Belluno province (Veneto Region, NE Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least "important" features by using a common threshold. Conclusively, we found that removing the least "important" features does not impact the overall accuracy of the LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least "important" ones. This confirms that the requirement for the important factor maps can be assessed based on the physiography of the region. Based on the analysis of the three models, it was observed that most commonly available feature data can be useful for carrying out LSM at regional scale, eliminating the least available ones in most of the use cases due to data scarcity. Identifying LSMs at regional scale has implications for understanding landslide phenomena in the region and post-event relief measures, planning disaster risk reduction, mitigation, and evaluating potentially affected areas.

scholarly journals The Effect of Aspect on Landslide and Its Relationship with Other Parameters

2021 ◽  
Author(s):  
Seda Cellek
Keyword(s):  
Landslide Susceptibility ◽  
Morphological Structure ◽  
Landslide Area ◽  
Conditioning Factors ◽  
Landslides Susceptibility ◽  
Susceptibility Maps ◽  
Sensitivity Studies ◽  
Landslide Susceptibility Maps

Aspect is one of the parameters used in the preparation of landslide susceptibility maps. The procedure of this easily accessible and conclusive parameter is still a matter of debate in the literature. Each landslide area has its own morphological structure, so it is not possible to make a generalization for the aspect. In other words, there is no aspect in which landslides develop in particular. Generally, landslides occur in areas facing more than one direction. The biggest reason for this is that those areas are under the influence of other parameters. Therefore, it is wrong to evaluate the aspect, alone. Since it is a part of the system, it should be evaluated together with other conditioning factors. In this research, many landslides susceptibility studies have been investigated. The directions and causes of landslides have been determined from the studies. In addition, the criteria of the used aspect classes have been investigated. In the literature, the number of class intervals chosen, and their reasons were investigated, and the effects of this parameter were tried to be revealed in new sensitivity studies.

Spatio-temporal landslide forecasting based on combination of rainfall thresholds and landslide susceptibility maps: a test in the Northern Apennines (Italy).

2020 ◽  
Author(s):  
Ascanio Rosi ◽  
Samuele Segoni ◽  
Veronica Tofani ◽  
Filippo Catani
Keyword(s):  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
End Users ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Susceptibility Maps ◽  
Coarse Spatial Resolution ◽  
Landslide Forecasting ◽  
Spatio Temporal ◽  
Landslide Susceptibility Maps

<p>Landslide forecasting and early warning at regional scale are difficult task and they are usually accomplished by the mean of statistical approaches aimed to define rainfall thresholds and landslide susceptibility maps.<br> Landslide susceptibility maps are based on the analysis of predisposing factors to assess the spatial probability of landslide occurrence, while rainfall thresholds are based on the correlation, valid on a wide area, between landslide occurrence and triggering factors, which usually are a couple of rainfall parameters, such as rainfall duration and intensity.<br>Susceptibility maps are static map that can be used for the spatial prediction of the most landslide prone areas, nut cannot be used to predict the temporal occurrence of a landslide triggering.<br>Rainfall thresholds can be used for temporal prediction, but with a coarse spatial resolution (usually some hundreds or thousands of km<sup>2</sup>), and the reference areas could contains both plains and hillslopes, so the alerts could involves both areas, even if landslides are improbable in river plains; this means that rainfall thresholds are not very suitable to identify the most probable triggering sites.<br>Rainfall thresholds and susceptibility maps can be therefore conveniently combined into dynamic hazard matrixes to obtain spatio-temporal forecasts of landslide hazard.<br>To combine these inputs, they are combined in a purposely-built hazard matrix, where each parameter is classified into 3 classes: landslide susceptibility map has been classified in S1 (low susceptibility), S2 (medium susceptibility) and S3 (high susceptibility), while rainfall rate has been classified in the classes R1, R2 and R3, by the definition of 2 rainfall thresholds.<br>The combination of the aforementioned classes allowed to define a matrix with 5 hazard classes, from H0 (null hazard) to H4 (high hazard), which was calibrated so that there was not any landslide in the H0 class and that the 90% of the landslide were in H2-H4 classes.<br>The result of this procedure is a dynamic hazard map, where the hazard, which is calculated for each pixel, can change over the time, based on rainfall rate variations.<br>For operational purposes, such a map cannot be used, since the pixel based resolution is too fine to be used during an emergency or to plan any activity in the planification phase, so the results have been aggregated at municipality scale, which is more easily readable for the end-users as local administrators and decision makers.<br>In this way it is possible to overcome the issues due to the stillness of susceptibility maps and to the coarse spatial resolution of rainfall thresholds, also avoiding results which could be hardly understandable outside of the scientific community.<br>This procedure was tested in a test site located in Northern Tuscany (Italy) and the work showed the possibility of obtaining results which are balanced between the scientific soundness and the needs of end-users like mayors, local administrators and civil protection personnel.</p><p> </p>

scholarly journals Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping

2021 ◽  
Vol 13 (7) ◽  
pp. 3803
Author(s):  
Rui-Xuan Tang ◽  
E-Chuan Yan ◽  
Tao Wen ◽  
Xiao-Meng Yin ◽  
Wei Tang
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Slope Length ◽  
Landslide Occurrence ◽  
Conditioning Factors ◽  
Slope Structure

This study validated the robust performances of the recently proposed comprehensive landslide susceptibility index model (CLSI) for landslide susceptibility mapping (LSM) by comparing it to the logistic regression (LR) and the analytical hierarchy process information value (AHPIV) model. Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors.

scholarly journals Assessing the quality of landslide susceptibility maps – case study Lower Austria

2013 ◽  
Vol 1 (2) ◽  
pp. 1001-1050 ◽  
Author(s):  
H. Petschko ◽  
A. Brenning ◽  
R. Bell ◽  
J. Goetz ◽  
T. Glade
Keyword(s):  
Confidence Intervals ◽  
Landslide Susceptibility ◽  
Spatial Planning ◽  
Cross Validation ◽  
Susceptibility Map ◽  
Landslide Occurrence ◽  
High Interest ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract. Landslide susceptibility maps are helpful tools to identify areas which might be prone to future landslide occurrence. As more and more national and provincial authorities demand for these maps to be computed and implemented in spatial planning strategies, the quality of the landslide susceptibility map and of the model applied to compute them is of high interest. In this study we focus on the analysis of the model performance by a repeated k-fold cross-validation with spatial and random subsampling. Furthermore, the focus is on the analysis of the implications of uncertainties expressed by confidence intervals of model predictions. The cross-validation performance assessments reflects the variability of performance estimates compared to single hold-out validation approaches that produce only a single estimate. The analysis of the confidence intervals shows that in 85% of the study area, the 95% confidence limits fall within the same susceptibility class. However, there are cases where confidence intervals overlap with all classes from the lowest to the highest class of susceptibility to landsliding. Locations whose confidence intervals intersect with more than one susceptibility class are of high interest because this uncertainty may affect spatial planning processes that are based on the susceptibility level.

scholarly journals AERIAL PHOTOGRAMMETRY AND MACHINE LEARNING BASED REGIONAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT FOR AN EARTHQUAKE PRONE AREA IN TURKEY

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 713-720
Author(s):  
G. Karakas ◽  
S. Kocaman ◽  
C. Gokceoglu
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Landslide Susceptibility ◽  
Data Driven ◽  
Natural Phenomenon ◽  
Landslide Occurrence ◽  
Conditioning Factors ◽  
Aerial Photogrammetry ◽  
Prone Area ◽  
Using Data

Abstract. Landslide is a frequently observed natural phenomenon and a geohazard with destructive effects on economies, society and the environment. Production of up-to-date landslide susceptibility (LS) maps is an essential process for landslide hazard mitigation. Obtaining up-to-date and accurate data for the production of LS maps is also important and this task can be achieved by using aerial photogrammetric techniques, which can produce geospatial data with high resolution. The produced geospatial datasets can be integrated in data-driven methods for obtaining accurate LS maps. In the present study, LS map was produced by using data-driven machine learning (ML) methods, i.e. random forest (RF). An earthquake and landslide prone area from the south-eastern part of Turkey was selected as the study area. Topographical derivatives were extracted from digital surface models (DSMs) produced by using aerial photogrammetric datasets with 30 cm ground sampling distances. The lithological parameters were employed in the study together with an accurate landslide inventory, which were also delineated by using the high-resolution DSMs and orthophotos. The relationships between the landslide occurrence and the pre-defined conditioning factors were analyzed using the frequency ratio (FR) method. The results show that the RF method exhibits high prediction performance in the study area with an area under curve (AUC) value of 0.92.

scholarly journals Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1723 ◽  
Author(s):  
Mohammad Mehrabi ◽  
Biswajeet Pradhan ◽  
Hossein Moayedi ◽  
Abdullah Alamri
Keyword(s):  
Fuzzy Inference System ◽  
Landslide Susceptibility ◽  
Fuzzy Inference ◽  
State Of The Art ◽  
Spatial Prediction ◽  
Inference System ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Neuro Fuzzy ◽  
Landslide Susceptibility Maps

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.

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