Landslide Risk Assessment Using Granular Fuzzy Rule-Based Modeling: A Case Study on Earthquake-Triggered Landslides

Shi, Bingxin; Zeng, Ting; Tang, Chuan; Zhang, Lifang; Xie, Zhipeng; Lv, Guorong; Wu, Qihong

doi:10.1109/access.2021.3116869

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Neural Networks [23], [24], [25], [26], Boosted Regression Trees [27], [28], the Wavelet Transform model [29], [30] and Random Forest [31], [32], [33] are among the AI algorithms applied to tackle the intricate problems of landslide assessment. The Random Subspace Space Fuzzy Rule-based Classifier (RSSCE) is utilized to predict landslide occurrences based on the analysis of rainfall data triggered by heavy rainfalls in hilly areas [34], [35].…”

Section: Related Workmentioning

confidence: 99%

Predictive Modeling of Landslide Susceptibility in Soft Soil Canal Regions: A Focus on Early Warning Systems

Anh,

Danh,

Nguyen

2023

IJACSA

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The Mekong Delta (MD) has suffered significant losses in land resources, economic damage, and human and property casualties due to recent landslides. An early warning system for landslides is a valuable tool for identifying the effectiveness and timely detection of changes in the soil to promptly determine solutions and minimize damage caused by landslides in an area. In this study, we apply a machine learning approach based on the Long Short-Term Memory (LSTM) algorithm to experiment with early warning of landslide events on soft soil in the MD. Horizontal pressure, the change in inclination angles of the sensor pile due to the soil mass sliding in both the x and y directions, and the warning levels are determined based on the deformation and displacement of the soil along the riverbank, considered candidate factors for inputs in the model. Data from the established sensor system is used to train the model, creating a training and testing dataset of 374,415 samples. The accuracy of the detection and classification threshold of the system is proposed to be measured using the average F1 score derived from precision and recall values. The optimal prediction results are gleaned from an observational window of 4 minutes and 30 seconds to project roughly 2 hours into the future. The validation process resulted in recall, precision, and F1-score stands at 0.8232 with a remarkably low standard deviation of about 1%. The successful application of this research can help identify abnormal events leading to riverbank landslides due to loading, thereby creating conditions for developing a reliable information system to provide managers with the ability to suggest timely solutions to protect the lives, property of residents and infrastructures.

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Section: Related Workmentioning

confidence: 99%

Predictive Modeling of Landslide Susceptibility in Soft Soil Canal Regions: A Focus on Early Warning Systems

Anh,

Danh,

Nguyen

2023

IJACSA

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“…N ATURAL disasters that occur around the globe are adverse events which are caused by natural factors in our environment that can have massive impact on human lives [1], economic losses and the overall welfare of any given society. Some examples of natural disasters are hurricanes, landslides, tsunamis, earthquakes, floods and tornadoes.…”

Section: Introductionmentioning

confidence: 99%

A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services

et al. 2023

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A considerable portion of the world frequently experiences flooding during the monsoon season. As a result of this catastrophic event, hundreds of individuals have become homeless. In addition, rescuers are not usually effective enough to rescue the majority of victims. This is due to inadequate rescue operations infrastructure, a severe flaw in today's technologically advanced society. This manuscript proposes a microservice-dependent secure rescue framework that uses geographic information system mapping with a K-Means clustering algorithm to identify flood-prone regions. Numerous microservices, such as fleet management, cloud computing, and data security, integrate and execute the framework in preand post-flood situations. Labeling data from the proposed framework generates a support vector machinebased classifier for predicting flood risk. Furthermore, a hybrid A* algorithm is developed to find an optimal route for the rescue operation. Based on the K-means clustering results, which reduced the variance by 89.2 percent overall, dividing the data into six clusters was the best option for this study. The smoothness of the suggested hybrid algorithm is also used to verify its superiority.

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“…Multiple factors can trigger landslides, including rainfall, earthquake shaking, water level changes, storm waves, rapid stream erosion, and human factors. Among them, earthquake-induced landslides are the most hazardous due to the rapid occurrence and large size of slope failures (Shi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Granular risk assessment of earthquake induced landslide via latent representations of stacked autoencoder

Feng,

Yang,

Wan

et al. 2023

Front. Environ. Sci.

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Earthquake-induced landslides are ubiquitous on slopes in terrestrial environments, which can pose a serious threat to local communities and infrastructures. Data-driven landslide assessments play a crucial role in preventing future landslide occurrences and recurrences. We present a novel granular computing approach that assesses landslide risk by combining fuzzy information granulation and a stacked autoencoder algorithm. The stacked autoencoder is trained using an end-to-end learning strategy to obtain a central latent vector with reduced dimensionality. The multivariate landslide dataset was used as both the input and output to train the stacked autoencoder algorithm. Subsequently, in the central latent vector of the stacked autoencoder, the Fuzzy C-means clustering algorithm was applied to cluster the landslides into various groups with different risk levels, and the intervals for each group were computed using the granular computing approach. An empirical case study in Wenchuan County, Sichuan, China, was conducted. A comparative analysis with other state-of-the-art approaches including Density-based spatial clustering of applications with noise (DBSCAN), K-means clustering, and Principal Component Analysis (PCA), is provided and discussed. The experimental results demonstrate that the proposed approach using a stacked autoencoder integrated with fuzzy information granulation provides superior performance compared to those by other state-of-the-art approaches, and is capable of studying deep patterns in earthquake-induced landslide datasets and provides sufficient interpretation for field engineers.

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Landslide Risk Assessment Using Granular Fuzzy Rule-Based Modeling: A Case Study on Earthquake-Triggered Landslides

Cited by 4 publications

References 32 publications

Predictive Modeling of Landslide Susceptibility in Soft Soil Canal Regions: A Focus on Early Warning Systems

Predictive Modeling of Landslide Susceptibility in Soft Soil Canal Regions: A Focus on Early Warning Systems

A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services

Granular risk assessment of earthquake induced landslide via latent representations of stacked autoencoder

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