An innovative, fast method for landslide susceptibility mapping using GIS-based LSAT toolbox

Polat, Ali

doi:10.1007/s12665-021-09511-y

Cited by 18 publications

(10 citation statements)

References 74 publications

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“…The process of tuning involves selecting the ideal model parameters. It entails utilizing the specified parameter values for an estimator to determine the optimal parameter values (Polat, 2021). Because of the large number of pixels in the study area, we limited the size of our simulated hidden layer to a maximum of 25 layers.…”

Section: Resultsmentioning

confidence: 99%

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Manandhar,

Huynh,

Bhattarai

et al. 2024

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PurposeThis research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.Design/methodology/approachUsing the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).FindingsThe validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.Research limitations/implicationsLand cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.Practical implicationsIt helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.Social implicationsThe social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.Originality/valueLandslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

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Section: Resultsmentioning

confidence: 99%

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Manandhar,

Huynh,

Bhattarai

et al. 2024

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“…However, this subjective experience leads to the uncertainty of the model [15]. Mathematical statistical models [16] including the information value method (IV) [17] and deterministic coefficient method, among others, rely on the engineering analogy method and superimpose factors in different ways to express the nonlinear relationship between factors and landslides. Machine learning models such as logistic regression [18], SVM [19], and RF [16] can efficiently capture the relationship between factors and landslides, which are widely used in landslide susceptibility assessment on account of their excellent performance and efficient modeling process [20], although partial models mat be challenging to interpret due to the black-box analysis process.…”

Section: Introductionmentioning

confidence: 99%

Landslide Dynamic Susceptibility Mapping Base on Machine Learning and the PS-InSAR Coupling Model

Miao,

Ruan,

et al. 2023

Remote Sensing

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Complex and fragile geological conditions combined with periodic fluctuations in reservoir water levels have led to frequent landslide disasters in the Three Gorges Reservoir area. With the development of remote sensing technology, many scholars have applied it to landslide susceptibility assessment to improve model accuracy; however, how to couple these two to obtain the optimal susceptibility assessment model remains to be studied. Based on Sentinel-1 data, relevant data, and existing research results, the information value method (IV), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) models were selected to analyze landslide susceptibility in the urban area of Wanzhou. Models with superior performance will be coupled with PS-InSAR deformation data using two methods: joint training and weighted overlay. The accuracy of different models was assessed and compared with the aim of determining the optimal coupling model and the role of InSAR in the model. The results indicate that the accuracy of different landslide susceptibility prediction models is ranked as RF > SVM > CNN > IV. Among the coupled dynamic models, the performance ranking was as follows: InSAR jointly trained RF (IJRF) > InSAR weighted overlay RF (IWRF) > InSAR jointly trained SVM (IJSVM) > InSAR weighted overlay SVM (IWSVM). Notably, the IJRF model, which combines InSAR deformation data through joint training, exhibited the highest accuracy, with an AUC value of 0.995. In the factor importance analysis within the IJRF model, InSAR deformation data ranked third after hydrological distance (0.210) and elevation (0.163), with a value of 0.154. A comparison between landslide dynamic susceptibility mapping (LDSM) and landslide susceptibility mapping (LSM) revealed that the inclusion of InSAR deformation data effectively reduced false positives around the landslide areas. The results suggest that joint training is the most suitable coupling method, allowing for the optimal expression of InSAR deformation data and enhancing the predictive accuracy of the model. This study serves as a reference for future research and provides a foundation for landslide risk management.

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“…By summarizing the previous studies, it can be discovered that more and more data-driven methods have been applied in debris-flow susceptibility assessment, and the main methods of machine learning at present are support vector machine (SVM), BPNN, logistic regression (LR), decision tree, etc. (Gorsevski et al 2016;Huang et al 2018a;Zêzere et al 2017;Polat et al 2021;Althuwaynee et al 2014;Zhou et al 2022). However, most of them use a single learner or multiple learners, to construct the debris-flow susceptibility models and compare the accuracy of each model.…”

Section: Introductionmentioning

confidence: 99%

Debris-Flow Susceptibility assessment Using Stacking Ensemble Learning Including Multiple Heterogeneous Learners with RFE for factor optimization

Zhao

Lin

2023

Preprint

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An accurate assessment of debris-flow susceptibility is of great importance to the prevention and control of debris-flow disasters in mountainous areas. In this study, by applying the recursive feature elimination-random forest (RFE-RF) and the stacking ensemble learning including multiple heterogeneous learners, debris-flow the high accuracy of the debris-flow susceptibility is assessed. As indicated by the results, the very low and low susceptibility zones of debris-flow are mainly concentrated in the eastern and western parts in the study area. The very high and high susceptibility zones are mainly distributed on the two banks of Xiaojiang River Valley and the south bank of Jinsha River, where there is fragile geological environment and high risk, in the study area. The medium susceptibility zone is mainly distributed around the very high and high susceptibility zones. There are excellent accuracy and stability in the stacking ensemble learning model of debris-flow susceptibility in the mountainous areas, when combining with the RFE-RF model and the diversity measurement. As for the stacking ensemble learning therein, the area under curve (AUC) value of the receiver-operating characteristic (ROC), the accuracy (ACC) value, and F1 score are the maximum, reaching 95.6%, 88.6%, and 88.9%, respectively. Besides, the root mean square error (RMSE) value is the minimum, namely 0.287, which indicates that stacking ensemble learning including multiple heterogeneous learners is a high-performance model for debris-flow susceptibility assessment. The findings can provide a scientific basis for the disaster prevention and mitigation in the mountainous areas.

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An innovative, fast method for landslide susceptibility mapping using GIS-based LSAT toolbox

Cited by 18 publications

References 74 publications

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

Landslide Dynamic Susceptibility Mapping Base on Machine Learning and the PS-InSAR Coupling Model

Debris-Flow Susceptibility assessment Using Stacking Ensemble Learning Including Multiple Heterogeneous Learners with RFE for factor optimization

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