Combining spatial response features and machine learning classifiers for landslide susceptibility mapping

Wei, Ruilong; Ye, Chengming; Sui, Tianbo; Ge, Yong; Li, Yao; Li, Jonathan

doi:10.1016/j.jag.2022.102681

Cited by 29 publications

(19 citation statements)

References 75 publications

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“…Different modeling methodologies may produce different outcomes. The predicted performance of these hybrid machine learning models was shown to be more accurate than traditional statistical models for landslide prediction modeling in the majority of cases 64,86 . Because machine learning approaches are designed to automatically detect correlations between effective factors 151 .…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the hybrid models higher performance in predicting landslide revealed that the landslide modeling may be improved by factor optimization 85 . Many studies have demonstrated the value of a hybrid strategy in landslide situations all over the world 10 , 82 , 83 , 86 . Due to regional geological and geomorphological causes, slope unsteadiness, including landslides, is a common problem in Uttarkashi, Uttarakhand 87 – 89 .…”

Section: Introductionmentioning

confidence: 99%

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Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Kainthura

Sharma

2022

Sci Rep

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Natural disasters always have a damaging effect on our way of life. Landslides cause serious damage to both human and natural resources around the world. In this paper, the prediction accuracy of five hybrid models for landslide occurrence in the Uttarkashi, Uttarakhand (India) was evaluated and compared. In this approach, the Rough Set theory coupled with five different models namely Bayesian Network (HBNRS), Backpropagation Neural Network (HBPNNRS), Bagging (HBRS), XGBoost (HXGBRS), and Random Forest (HRFRS) were taken into account. The database for the models development was prepared using fifteen conditioning factors that had 373 landslide and 181 non-landslide locations that were then randomly divided into training and testing locations with a ratio of 75%:25%. The appropriateness and predictability of these conditioning factors were assessed using the multi-collinearity test and the least absolute shrinkage and selection operator approach. The accuracy, sensitivity, specificity, precision, and F-Measures, and the area under the curve (AUC)-receiver operating characteristics curve, were used to evaluate and compare the performance of the individual and hybrid created models. The findings indicate that the constructed hybrid model HXGBRS (AUC = 0.937, Precision = 0.946, F1-score = 0.926 and Accuracy = 89.92%) is the most accurate model for predicting landslides when compared to other models (HBPNNRS, HBNRS, HBRS, and HRFRS). Importantly, when the fusion is performed with the rough set method, the prediction capability of each model is improved. Simultaneously, the HXGBRS model proposed shows superior stability and can effectively avoid overfitting. After the core modules were developed, the user-friendly platform was designed as an integrated GIS environment using dynamic maps for effective landslide prediction in large prone areas. Users can predict the probability of landslide occurrence for selected region by changing the values of a conditioning factors. The created approach could be beneficial for predicting the impact of landslides on slopes and tracking landslides along national routes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Kainthura

Sharma

2022

Sci Rep

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“…Peng et al [41] Novel hybrid method combining rough set theory and SVM Yu et al [40] SVM with geographical weighted regression and PSO Pham et al [86] Novel hybrid method using sequential minimal optimization and SVM Zhang et al [117] Fractal dimension with index of entropy and SVM Adnan et al [118] LSM generated by combining the LSM produced by four ML models KNN, MLP, RF, and SVM Wang et al [119] GeoSOM with RF and ensemble ML model consisting of ANN-SVM-GBDT Fang et al [13] Proposed three hybrid models CNN-SVM, CNN-RF, and CNN-LGR Hu et al [48] Combining fractal theory with SVM and NB Rong et al [74] Combination of Bayesian optimization with RF and GBDT Wang et al [55] Integration of MultiBoost with RBFN and CDT Sahana et al [120] Multi-layer perceptron neural network classifier with ensemble ML models like Bagging, Dagging, and DECORATE Xie et al [79] GeoDetector using factor detectors and interaction detectors with four ML models ANN, BN, LGR, and SVM Alqadhi et al [121] Four optimized ML model namely PSO-ANN, PSO-RF, PSO-M5P, and PSO-SVM with LGR Arabameri et al [122] Credal decision tree based hybrid models namely CDT-bagging, CDT-MultiBoost, and CDT-SubSpace Saha et al [123] Hybrid ensemble method using RF as a base classifier and ensemble methods, namely RotFor-RF, RSS-RF, and bagging-RF Xing et al [124] The output of ML models namely back propagation, RF, and SVM are combined using weight factors Hu et al [125] Fuzzy c-means clustering and factor analysis with LGR Zhou et al [51] RF with GeoDetector and recursive feature elimination Sun et al [126] GeoDetector and RF Lui et al [61] GeoDetector with RF Liang et al [71] Combination of unsupervised and supervised ML method Dung et al [127] Novel hybrid method consisting bagging-based rough set and AdaBoost-based rough set Wei et al [128] Spatial response feature with ML classifiers…”

Section: Author Year Hybrid Methodsmentioning

confidence: 99%

“…Wei et al [128] used a hybrid method comprising spatial response (SR) feature with ML classifiers (SR-ML). The technique consists of three steps: extracting spatial features using depthwise separable convolution, extracting features on a different scale using spatial pyramid pooling, and ML classification using the features extracted in the previous steps.…”

Section: Author Year Hybrid Methodsmentioning

confidence: 99%

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Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

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Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

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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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Combining spatial response features and machine learning classifiers for landslide susceptibility mapping

Cited by 29 publications

References 75 publications

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

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

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