Landslide susceptibility prediction improvements based on a semi-integrated supervised machine learning model

Yang, Ning; Wang, Rui; Liu, Zhaofei; Yao, Zhijun

doi:10.1007/s11356-023-25650-0

Cited by 9 publications

(3 citation statements)

References 46 publications

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“…Previous research results had shown that the use of the SSML non-landslide selection method in SSML might reduce the uncertainty of the model and improve its prediction accuracy [27,58,59]. However, the conclusions obtained in this study differed from previous ones.…”

Section: Effects Of Non-landslide Sample Selectioncontrasting

confidence: 80%

Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi

Ma,

Chen,

et al. 2023

Sustainability

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Landslide susceptibility prediction (LSP) is the basis for risk management and plays an important role in social sustainability. However, the modeling process of LSP is constrained by various factors. This paper approaches the effect of landslide data integrity, machine-learning (ML) models, and non-landslide sample-selection methods on the accuracy of LSP, taking the Yinghu Lake Basin in Ankang City, Shaanxi Province, as an example. First, previous landslide inventory (totaling 46) and updated landslide inventory (totaling 46 + 176) were established through data collection, remote-sensing interpretation, and field investigation. With the slope unit as the mapping unit, twelve conditioning factors, including elevation, slope, aspect, topographic relief, elevation variation coefficient, slope structure, lithology, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), distance to road, distance to river, and rainfall were selected. Next, the initial landslide susceptibility mapping (LSM) was obtained using the K-means algorithm, and non-landslide samples were determined using two methods: random selection and semi-supervised machine learning (SSML). Finally, the random forest (RF) and artificial neural network (ANN) machine-learning methods were used for modeling. The research results showed the following: (1) The performance of supervised machine learning (SML) (RF, ANN) is generally superior to unsupervised machine learning (USML) (K-means). Specifically, RF in the SML model has the best prediction performance, followed by ANN. (2) The selection method of non-landslide samples has a significant impact on LSP, and the accuracy of the SSML-based non-landslide selection method is controlled by the ratio of the number of landslide samples to the number of mapping units. (3) The quantity of landslides has an impact on how reliably the results of LSM are obtained because fewer landslides result in a smaller sample size for LSM, which deviates from reality. Although the results in this dataset are satisfactory, the zoning results cannot reliably anticipate the recently added landslide data discovered by the interpretation of remote-sensing data and field research. We propose that the landslide inventory can be increased by remote sensing in order to achieve accurate and impartial LSM since the LSM of adequate landslide samples is more reasonable. The research results of this paper will provide a reference basis for uncertain analysis of LSP and regional landslide risk management.

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Section: Effects Of Non-landslide Sample Selectioncontrasting

confidence: 80%

Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi

Ma,

Chen,

et al. 2023

Sustainability

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“…Landslides are some of the most frequent natural hazards in the world's mountainous regions, and particularly in Vietnam (Khaliq et al, 2023;Vincent et al, 2023). Landslides have increased in inten- According to previous studies, the most important conditioning factors include (i) factors that make ground surface vulnerable to damage, often including intrinsic subsoil characteristics in areas of slope instability, and (ii) triggering factors for landslides, such as external factors like climate, hydrology and human impact (Liu et al, 2022;Yang et al, 2023). Therefore, conditioning factors such as elevation, slope, lithology, soil type, rainfall, hydrology, distance to road and land use are often considered important.…”

Section: Discussionmentioning

confidence: 99%

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Nguyen,

et al. 2023

Geological Journal

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Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data to help decision‐makers, for example, local authorities and other organizations, mitigating the landslide risk, although the accuracy of existing studies needs to be improved. This study aims to assess landslide susceptibility in the Thua Thien Hue province of Vietnam using deep neural networks (DNNs) and swarm‐based optimization algorithms, namely Adam, stochastic gradient descent (SGD), Artificial Rabbits Optimization (ARO), Tuna Swarm Optimization (TSO), Sand Cat Swarm Optimization (SCSO), Honey Badger Algorithm (HBA), Marine Predators Algorithm (MPA) and Particle Swarm Optimization (PSO). The locations of 945 landslides occurring between 2012 and 2022, along with 14 conditioning factors, were used as input data to build the DNN and DNN‐hybrid models. The performance of the proposed models was evaluated using the statistical indices receiver operating characteristic curve, area under the curve (AUC), root mean square error, mean absolute error (MAE), R2 and accuracy. All proposed models had a high accuracy of prediction. The DNN‐MPA model had the highest AUC value (0.95), followed by DNN‐HBA (0.95), DNN‐ARO (0.95), DNN‐Adam (0.95), DNN‐SGD (0.95), DNN‐TSO (0.93), DNN‐PSO (0.9) and finally DNN‐SCSO (0.83). High‐precision models have identified that the majority of the western region of Thua Thien Hue province is very highly susceptible to landslides. Models like the aforementioned ones can support decision‐makers in updating large‐scale sustainable land‐use strategies.

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“…Model performance evaluation is key to predictive models (Yang et al 2023). In binary classification model evaluation, several commonly used statistical parameters are precision (P), recall (R), and accuracy(ACC).…”

Section: Model Performance Evaluation Metricsmentioning

confidence: 99%

Study on deep learning methods for rockburst risk prediction based on mining-induced seismicity quantification

Cheng

Qiao

2023

Preprint

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The assessment of rockburst risk (RBR) is the premise of rockburst prevention and control. It is the implementation criterion to guide various rockburst prevention and control measures. The existing static prediction and evaluation methods for RBR cannot be effectively combined with the results of underground dynamic monitoring. This study proposed a mining-induced seismicity information quantification method based on the fractal theory. Deep learning methods are used to construct a deep learning framework of rockburst risk (DLFR) based on the fractal dimension of microseismic information. Gray correlation analysis (GRA), information gain ratio (IGR), and Pearson correlation coefficient are used to screen and compare factors. Statistical evaluation indicators such as macro-F1, accuracy rate, and fitness curve were used to evaluate model performance. Taking Gaojiapu Coal Mine in the Binchang mining area as a case study, the performance of deep learning models such as BP Neural Network (BP), Support Vector Machine (SVM) and its optimized model based on particle swarm optimization (PSO) algorithm under this framework is discussed. The reliability and validity of the research results to verify by comparing the predicted results with the actual results. The research results show that the prediction results of RBR in DLFR are consistent with the actual results, and the model is reliable and effective. The mining-induced seismicity quantification can solve the problem of insufficient training samples for the RBR. With this, different pressure relief measures can be formulated based on the results of the RBR predictions to achieve "graded" precise prevention and control.

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Landslide susceptibility prediction improvements based on a semi-integrated supervised machine learning model

Cited by 9 publications

References 46 publications

Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi

Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Study on deep learning methods for rockburst risk prediction based on mining-induced seismicity quantification

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