scholarly journals Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-based FR–RF Integrated Model and Multiresolution DEMs

2019 ◽  
Vol 11 (9) ◽  
pp. 999 ◽  
Author(s):  
Arabameri ◽  
Pradhan ◽  
Rezaei ◽  
Lee
Keyword(s):  
Artificial Intelligence ◽  
Landslide Susceptibility ◽  
Thermal Emission ◽  
Integrated Model ◽  
Google Earth ◽  
Mitigation Measures ◽  
Landslide Susceptibility Mapping ◽  
Economic Losses ◽  
Landslide Risk ◽  
Normalised Difference Vegetation Index

Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1,072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most important controlling variables. Assessment of model performance by calculating the area under the receiving operating characteristic curve parameter showed that FR–RF integrated model (0.917) achieved higher predictive accuracy than the individual FR (0.865) and RF (0.840) models. Comparison of PALSAR, ASTER, and SRTM DEMs with 12.5, 30 and 90 m spatial resolution, respectively, with the FR–RF integrated model showed that the prediction accuracy of FR–RF–PALSAR (0.917) was higher than FR–RF–ASTER (0.865) and FR–RF–SRTM (0.863). The results of this study could be used by local planners and decision makers for planning development projects and landslide hazard mitigation measures.

scholarly journals Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3312
Author(s):  
Jiaying Li ◽  
Weidong Wang ◽  
Yange Li ◽  
Zheng Han ◽  
Guangqi Chen
Keyword(s):  
Landslide Susceptibility ◽  
Heavy Rainfall ◽  
Characteristic Curve ◽  
Landslide Susceptibility Mapping ◽  
Economic Losses ◽  
Support Vector ◽  
Landslide Risk ◽  
Chi Square ◽  
Area Index ◽  
The Impact

Landslide represents an increasing menace causing huge casualties and economic losses, and rainfall is a predominant factor inducing landslides. Landslide susceptibility assessment (LSA) is a commonly used and effective method to prevent landslide risk, however, the LSA does not analyze the impact of the rainfall on landslides which is significant and non-negligible. Therefore, the spatiotemporal LSA considering the inducing effect of rainfall is proposed to improve accuracy and applicability. In this study, the influencing factors are selected using the chi-square test, out-of-bag error and multicollinearity test. The spatial LSA are thus obtained using the random forest (RF) model, deep belief networks model and support vector machine, and compared using receiver operating characteristic curve and seed cell area index to determine the optimal assessment result. According to the heavy rainfall characteristics in the study area, the rainfall period is divided into four stages, and the effective rainfall model is employed to generate the rainfall impact (RI) maps of the four stages. The spatiotemporal LSAs are obtained by coupling the optimal spatial LSA and various RI maps and verified using the landslide warning map. The results demonstrate that the optimal spatiotemporal LSA is obtained using the spatial LSA of the RF model and temporal LSA of the rainfall data in the peak stage. It can predict the area where rainfall-induced landslides are likely to occur and prevent landslide risk.

scholarly journals Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake

2021 ◽  
Vol 13 (6) ◽  
pp. 1157
Author(s):  
Yimo Liu ◽  
Wanchang Zhang ◽  
Zhijie Zhang ◽  
Qiang Xu ◽  
Weile Li
Keyword(s):  
Random Forest ◽  
Landslide Susceptibility ◽  
Prediction Accuracy ◽  
Integrated Model ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk ◽  
Conditioning Factors ◽  
Q Statistic ◽  
Selection Of

Landslide susceptibility mapping is an effective approach for landslide risk prevention and assessments. The occurrence of slope instability is highly correlated with intrinsic variables that contribute to the occurrence of landslides, such as geology, geomorphology, climate, hydrology, etc. However, feature selection of those conditioning factors to constitute datasets with optimal predictive capability effectively and accurately is still an open question. The present study aims to examine further the integration of the selected landslide conditioning factors with Q-statistic in Geo-detector for determining stratification and selection of landslide conditioning factors in landslide risk analysis as to ultimately optimize landslide susceptibility model prediction. The location chosen for the study was Atsuma Town, which suffered from landslides following the Eastern Iburi Earthquake in 2018 in Hokkaido, Japan. A total of 13 conditioning factors were obtained from different sources belonging to six categories: geology, geomorphology, seismology, hydrology, land cover/use and human activity; these were selected to generate the datasets for landslide susceptibility mapping. The original datasets of landslide conditioning factors were analyzed with Q-statistic in Geo-detector to examine their explanatory powers regarding the occurrence of landslides. A Random Forest (RF) model was adopted for landslide susceptibility mapping. Subsequently, four subsets, including the Manually delineated landslide Points with 9 features Dataset (MPD9), the Randomly delineated landslide Points with 9 features Dataset (RPD9), the Manually delineated landslide Points with 13 features Dataset (MPD13), and the Randomly delineated landslide Points with 13 features Dataset (RPD13), were selected by an analysis of Q-statistic for training and validating the Geo-detector-RF- integrated model. Overall, using dataset MPD9, the Geo-detector-RF-integrated model yielded the highest prediction accuracy (89.90%), followed by using dataset MPD13 (89.53%), dataset RPD13 (88.63%) and dataset RPD9 (87.07%), which implied that optimized conditioning factors can effectively improve the prediction accuracy of landslide susceptibility mapping.

scholarly journals Towards A Geo-Hydro-Mechanical Characterization of Landslide Classes: Preliminary Results

2020 ◽  
Vol 10 (22) ◽  
pp. 7960
Author(s):  
Federica Cotecchia ◽  
Francesca Santaloia ◽  
Vito Tagarelli
Keyword(s):  
Risk Mitigation ◽  
Mitigation Strategies ◽  
Slope Instability ◽  
Mitigation Measures ◽  
Economic Losses ◽  
Landslide Risk ◽  
Multidisciplinary Analysis ◽  
Risk Mitigation Strategies ◽  
Landslide Mechanisms

Nowadays, landslides still cause both deaths and heavy economic losses around the world, despite the development of risk mitigation measures, which are often not effective; this is mainly due to the lack of proper analyses of landslide mechanisms. As such, in order to achieve a decisive advancement for sustainable landslide risk management, our knowledge of the processes that generate landslide phenomena has to be broadened. This is possible only through a multidisciplinary analysis that covers the complexity of landslide mechanisms that is a fundamental part of the design of the mitigation measure. As such, this contribution applies the “stage-wise” methodology, which allows for geo-hydro-mechanical (GHM) interpretations of landslide processes, highlighting the importance of the synergy between geological-geomorphological analysis and hydro-mechanical modeling of the slope processes for successful interpretations of slope instability, the identification of the causes and the prediction of the evolution of the process over time. Two case studies are reported, showing how to apply GHM analyses of landslide mechanisms. After presenting the background methodology, this contribution proposes a research project aimed at the GHM characterization of landslides, soliciting the support of engineers in the selection of the most sustainable and effective mitigation strategies for different classes of landslides. This proposal is made on the assumption that only GHM classification of landslides can provide engineers with guidelines about instability processes which would be useful for the implementation of sustainable and effective landslide risk mitigation strategies.

scholarly journals GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Trung-Hieu Tran ◽  
Nguyen Duc Dam ◽  
Fazal E. Jalal ◽  
Nadhir Al-Ansari ◽  
Lanh Si Ho ◽  
...  
Keyword(s):  
Soft Computing ◽  
Landslide Susceptibility ◽  
Google Earth ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Statistical Measures ◽  
Susceptibility Maps ◽  
Predicted Values ◽  
Landslide Susceptibility Maps ◽  
Computing Models

The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also.

scholarly journals Artificial intelligence approaches for spatial prediction of landslides in mountainous regions of western India

2021 ◽  
Author(s):  
PANKAJ PRASAD ◽  
Victor Joseph Loveson ◽  
Sumit Das ◽  
Priyankar Chandra
Keyword(s):  
Artificial Intelligence ◽  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Gradient Boosting ◽  
Susceptibility Map ◽  
Western India ◽  
Boosted Regression Tree ◽  
Study Region ◽  
Mountainous Regions ◽  
Stochastic Gradient Boosting

Abstract The prediction of landslide is a complex task but preparing the landslide susceptibility map through artificial intelligence approaches can reduce life loss and damages resulting from landslides. The purpose of this study is to evaluate and compare the landslide susceptibility mapping (LSM) using six machine learning models, including random forest (RF), deep boost (DB), stochastic gradient boosting (SGB), rotation forest (RoF), boosted regression tree (BRT), and logit boost (LB) in the mountainous regions of western India. The landslide inventory map consisting of 184 landslide locations has been divided into two groups for training (70% dataset) and validation (30% dataset) purposes. Fourteen landslide triggering factors including slope, topographical roughness index, road density, topographical wetness index, elevation, slope length, drainage density, stream power index, geomorphology, rainfall, soil, lithology, lineament density, and normalized difference vegetation index have been considered using the boruta approach for the LSM. The results reveal that the RF model has the highest precision in terms of AUC (0.88; 0.89), kappa (0.62; 0.50), accuracy (0.81; 0.77), and specificity (0.86; 0.86) both in the study region and secondary region, respectively. Hence, it can be concluded that the RF is an effective and promising technique as compared to DB, SGB, RoF, BRT, and LB for landslide susceptibility assessment in the research area as well as in regions having similar geo-environmental configuration.

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 A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods

2022 ◽  
Vol 14 (1) ◽  
pp. 211
Author(s):  
Pengxiang Zhao ◽  
Zohreh Masoumi ◽  
Maryam Kalantari ◽  
Mahtab Aflaki ◽  
Ali Mansourian
Keyword(s):  
Landslide Susceptibility ◽  
Variable Importance ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Economic Losses ◽  
Support Vector ◽  
Case Study Area ◽  
Causative Factors ◽  
Importance Analysis

Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management.

scholarly journals Landslide susceptibility mapping in eastern hills of Rara Lake, western Nepal

2016 ◽  
Vol 50 (1) ◽  
pp. 125-131
Author(s):  
Padam Bahadur Budha ◽  
Kabiraj Paudyal ◽  
Motilal Ghimire
Keyword(s):  
Success Rate ◽  
Landslide Susceptibility ◽  
Area Under The Curve ◽  
Google Earth ◽  
Landslide Susceptibility Mapping ◽  
Slope Failures ◽  
Individual Factor ◽  
Index Method ◽  
Mapping Process ◽  
Landslide Susceptibility Index

 The tendency of occurrences of slope failures in future in an area is landslides susceptibility. This tendency in eastern hills of Rara Lake was analyzed through mapping process. Bivariate statistical index method was used to produce the susceptibility of landslides. Ninety six slope failures were delineated from Google Earth imagery. The ratio of landslide densities of each individual factor classes to that of whole area gave weight values necessary to produce landslide susceptibility index (LSI). East and South facing slopes, gradients of <30o, elevation of 2000-2800 m, buffers closer to road and streams, grassland and cultivation lands, and lithology of pelitic metamorphic rocks were factor classes with positive weight values. The LSI values ranging from -7.12 to 5.45 were reclassified into five susceptibility classes. Landslide densities of 8.12 and 4.76 per km2 were observed for very high and high susceptible zones. Success rate curve, made from 35 landslides located in the field survey, showed 0.76 portion area under the curve. This gives 76% overall success rate. Higher susceptible areas are cultivated areas and grasslands, where most houses were located. Thus, knowing the landslide susceptibility of areas, necessary preparedness can be done to reduce the impacts.

A combined procedure to assess rainfall-induced shallow landslide detachment, transit and runout susceptibility using Machine Learning and GIS techniques

2021 ◽  
Author(s):  
Mariano Di Napoli ◽  
Diego Di Martire ◽  
Domenico Calcaterra ◽  
Marco Firpo ◽  
Giacomo Pepe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Mitigation Measures ◽  
Economic Losses ◽  
Meaningful Improvement ◽  
Susceptibility Evaluation ◽  
Landslide Runout

&lt;p&gt;Rainfall-induced landslides are notoriously dangerous phenomena which can cause a notable death toll as well as major economic losses globally. Usually, shallow landslides are triggered by prolonged or severe rainfalls and frequently may evolve into potentially catastrophic flow-like movements. Shallow failures are typical in hilly and mountainous areas due to the combination of several predisposing factors such as slope morphology, geological and structural setting, mechanical properties of soils, hydrological and hydrogeological conditions, land-use changes and wildfires. Because of the ability of these phenomena to travel long distances, buildings and infrastructures located in areas improperly deemed safe can be affected.&lt;/p&gt;&lt;p&gt;Spatial and temporal hazard posed by flow-like movements is due to both source characteristics (e.g., location and volume) and the successive runout dynamics (e.g., travelled paths and distances). Hence, the assessment of shallow landslide susceptibility has to take into account not only the recognition of the most probable landslide source areas, but also &amp;#160;landslide runout (i.e., travel distance). In recent years, a meaningful improvement in landslide detachment susceptibility evaluation has been gained through robust scientific advances, especially by using statistical approaches. Furthermore, various techniques are available for landslide runout susceptibility assessment in quantitative terms. The combination of landslide detachment and runout dynamics has been admitted by many researchers as a suitable and complete procedure for landslide susceptibility evaluation. However, despite its significance, runout assessment is not as widespread in literature as landslide detachment assessment and still remains a challenge for researchers. Currently, only a few studies focus on the assement of both landslide detachment susceptibility (LDS) and landslide runout susceptibility (LRS).&lt;/p&gt;&lt;p&gt;In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. Such procedure is based on the integration between LDS assessment via Machine Learning techniques (applying the Ensemble approach) and LRS assessment through GIS-based tools (using the &amp;#8220;reach angle&amp;#8221; method). This methodology has been applied to the Cinque Terre National Park (Liguria, north-west Italy), where risk posed by flow-like movements is very high. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. In particular, the obtained map may be useful for urban and regional planning, as well as for decision-makers and stakeholders, to predict areas that may be affected by rainfall-induced shallow landslides&amp;#160; in the future and to identify areas where risk mitigation measures are needed.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document