Landslide susceptibility investigation for Idukki district of Kerala using regression analysis and machine learning

Jones, Sheelu; Kasthurba, A. K.; Bhagyanathan, Anjana; Binoy, B V

doi:10.1007/s12517-021-07156-6

Cited by 32 publications

(4 citation statements)

References 58 publications

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“…We analyzed the core area of the Minxian earthquake landslide using recursive feature elimination in a conventional machine learning model to narrow down 20 factors to the most influential ones, as shown in Figure 4. Different models identified different influential factors, leading to varied optimization outcomes as noted in previous studies [25]. To overcome this, we normalized, accumulated, and integrated the importance of each factor from all models with the results from the geographic detectors (Figure 3), achieving a universally applicable set of evaluation factors.…”

Section: The Optimization Of the Evaluation Factors Of The Seismic La...mentioning

confidence: 99%

“…Seismic landslide, as a nonlinear problem, is challenging to be resolved accurately by conventional statistical methods [12], while the toolset of machine learning methods has powerful data processing capability and various types of models, which are widely used in seismic landslide research [13], which contains logistic regression model [14][15][16][17], random forest [18][19][20], support vector machine [21,22], artificial neural networks [23], Bayesian classification [24], and others. However, because machine learning models depend mainly upon databases, data training may have appeared as a disadvantage, such as overfitting or underfitting and not allowing to extract useful interpretation [25,26]. Therefore, the selection of crucial evaluation factors and reduction in redundant factors in the dataset can not only resolve the fitting problem of machine learning but also reduce the computational burden and improve the efficiency of the model.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

He,

Wang,

Wang

et al. 2024

Sustainability

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The accuracy of Seismic Landslide Susceptibility Maps (SLSMs) is imperative for the prevention of seismic landslide disasters. This study enhances the precision of SLSMs by integrating nine distinct machine learning methodologies with the GeoDetector version 0.0.4 tool to filter both numerical and physical factors contributing to landslide susceptibility. The dataset comprises 2317 landslide instances triggered by the 2013 Minxian Ms = 6.6 earthquake, from which redundant factors were pruned using the Recursive Feature Elimination technique. Subsequent evaluations of the optimized factors, both individually and in combination, were conducted through Frequency Ratio analysis and Factor Interaction assessment. The study juxtaposes the Area Under the Receiver Operating Characteristic Curve (AUC) and the accuracy of nine machine learning models before and after factor optimization. The findings indicate an increase in AUC from a maximum of 0.989 to 0.992 in the Random Forest model, and an 8.37% increase in AUC for the SVM model, signifying a notable enhancement in the stability across all models. The establishment of the SLSM notably elevated the frequency ratio in high-risk zones from 50.40 to 85.14, underscoring the efficacy of combining machine learning and detector optimization techniques in sustainable practices. This research proposes a universal framework aimed at eliminating redundancy and noise in SLSMs and hazard risk assessments, thereby facilitating sustainable geological disaster risk management.

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Section: The Optimization Of the Evaluation Factors Of The Seismic La...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

He,

Wang,

Wang

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…In recent years, artificial intelligence (AI) technology has been increasingly applied to various geological problems due to its ability to effectively tackle nonlinear problems, such as earthquake prediction (Laurenti et al, 2022), landslide hazard assessment (Collico et al, 2020;Ji et al, 2020;Jones et al, 2021;Du et al, 2022), meteorological and climate forecasting (Balogun et al, 2021;Zennaro et al, 2021), and geophysical data interpretation (Lawson et al, 2017;Maxwell et al, 2019;Politikos et al, 2021;Du et al, 2023). Scholars have made progress in the field of geological time series prediction using AI techniques, for example, wave height prediction (Gao et al, 2021), pore pressure prediction in landslides (Orland et al, 2020;Wei et al, 2021), and prediction of landslide displacement (Yang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Neural network models for seabed stability: a deep learning approach to wave-induced pore pressure prediction

Du,

Sun,

Song

et al. 2023

Front. Mar. Sci.

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Wave cyclic loading in submarine sediments can lead to pore pressure accumulation, causing geohazards and compromising seabed stability. Accurate prediction of long-term wave-induced pore pressure is essential for disaster prevention. Although numerical simulations have contributed to understanding wave-induced pore pressure response, traditional methods lack the ability to simulate long-term and real oceanic conditions. This study proposes the use of recurrent neural network (RNN) models to predict wave-induced pore pressure based on in-situ monitoring data. Three RNN models (RNN, LSTM, and GRU) are compared, considering different seabed depths, and input parameters. The results demonstrate that all three RNN models can accurately predict wave-induced pore pressure data, with the GRU model exhibiting the highest accuracy (absolute error less than 2 kPa). Pore pressure at the previous time step and water depth are highly correlated with prediction, while wave height, wind speed, and wind direction show a secondary correlation. This study contributes to the development of wave-induced liquefaction early warning systems and offers insights for utilizing RNNs in geological time series analysis.

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“…In recent years, artificial intelligence methods have made significant strides in geological fields, including remote sensing [5][6][7][8], geological hazard prediction [9][10][11][12][13][14][15][16], geological exploration [17][18][19][20][21][22], and energy development [23]. However, the applicability and effectiveness of these methods in the specialized field of pipeline or cable (POC) detection remain inadequately explored.…”

Section: Introductionmentioning

confidence: 99%

Revealing the Potential of Deep Learning for Detecting Submarine Pipelines in Side-Scan Sonar Images: An Investigation of Pre-Training Datasets

Du,

Sun,

Song

et al. 2023

Remote Sensing

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This study introduces a novel approach to the critical task of submarine pipeline or cable (POC) detection by employing GoogleNet for the automatic recognition of side-scan sonar (SSS) images. The traditional interpretation methods, heavily reliant on human interpretation, are replaced with a more reliable deep-learning-based methodology. We explored the enhancement of model accuracy via transfer learning and scrutinized the influence of three distinct pre-training datasets on the model’s performance. The results indicate that GoogleNet facilitated effective identification, with accuracy and precision rates exceeding 90%. Furthermore, pre-training with the ImageNet dataset increased prediction accuracy by about 10% compared to the model without pre-training. The model’s prediction ability was best promoted by pre-training datasets in the following order: Marine-PULSE ≥ ImageNet > SeabedObjects-KLSG. Our study shows that pre-training dataset categories, dataset volume, and data consistency with predicted data are crucial factors affecting pre-training outcomes. These findings set the stage for future research on automatic pipeline detection using deep learning techniques and emphasize the significance of suitable pre-training dataset selection for CNN models.

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Landslide susceptibility investigation for Idukki district of Kerala using regression analysis and machine learning

Cited by 32 publications

References 58 publications

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

Neural network models for seabed stability: a deep learning approach to wave-induced pore pressure prediction

Revealing the Potential of Deep Learning for Detecting Submarine Pipelines in Side-Scan Sonar Images: An Investigation of Pre-Training Datasets

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