Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Zhao, Baoxin; Zhu, Jingzhong; Hu, Youbiao; Liu, Qimeng; Liu, Yu

doi:10.1155/2022/2058442

Cited by 12 publications

(6 citation statements)

References 91 publications

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“…The reliability and "accuracy" of these approaches are crucial and must be assessed accordingly. Different machine learning techniques were used by Zhao et al (2022), including LR, "support vector machines-SVMs," RF, and a coupled model of the "whale optimization algorithm"-WOA and "genetic algorithm" genetic algorithm-GA with RF. Their model's testing rate of logistic regression was 75.5%, which was less than our model's LR's 82.0%.…”

Section: Discussionmentioning

confidence: 99%

Landslide susceptibility, ensemble machine learning, and accuracy methods in the southern Sinai Peninsula, Egypt: Assessment and Mapping

Youssef,

El‑Haddad,

Skilodimou

et al. 2023

Preprint

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Each year, thousands of tourists visit Egypt's Wadi Feiran region, one of the most popular tourist sites in the Sinai Peninsula. The region’s topography is distinctive and diverse, making it particularly susceptible to “natural disasters” (such as floods and landslides). The current study deals with landslide hazards as a critical hazard type, where, after rainfall, hundreds of landslides occur annually, and landslide disaster assessments are becoming more necessary to reduce mountain hazards. The current research mapped "landslide susceptibility" in the Wadi Feiran basin using three different modeling strategies: “Logistic Regression” -LR, “Artificial Neural Network”-ANN, and an "ensemble" of LR and ANN. A “landslides” map was first created as a preliminary stage, using 800 landslide locations acquired from multiple data sources (30% validation datasets, 70% training datasets), including historical records, field surveys, and high-resolution satellite imagery. In addition, fourteen landslide causative parameters (LCPs), elevation (El), “distance to wadis” (DtW), “distance to fault” (DtF), “distance to road” (DtR), lithology (Lith), aspect (As), “profile-curvature” (PrC), “plan-curvature” (PlC), “slope length” (LS), slope-angle (Sa), “topographic wetness index” (TWI), “relative slope position” (RSP), rainfall (Ra), and “topographic roughness index” (TRI) were employed. These models' accuracy was evaluated using “receiver operating characteristics and area under the curve (ROC - AUC),” “root mean square error”-RMSE, and “kappa index”-K. According to the findings, the AUC for LR, ANN, and ensemble of LR &ANN were 82%, 89%, and 91%, respectively. The results showed that the ensemble model outperformed ANN and LR by 2.3% and 10.9%, respectively, whereas ANN model outperformed LR by 8.5%. Other statistical indices also revealed that the RMSE and kappa index values obtained by LR were the highest and the lowest, respectively, whereas the RMSE and kappa index values generated by the LR&ANN ensemble were the lowest and the highest, respectively. These results indicate that landslides are influenced by a wide variety of natural and anthropogenic factors. To better manage and avoid landslides, it is important to create maps that show the area’s most susceptible to landslides.

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Section: Discussionmentioning

confidence: 99%

Landslide susceptibility, ensemble machine learning, and accuracy methods in the southern Sinai Peninsula, Egypt: Assessment and Mapping

Youssef,

El‑Haddad,

Skilodimou

et al. 2023

Preprint

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“…Ensuring the accuracy of a model is a critical step in data analysis, and data validation is one of the most important ways to achieve it. Multiple methods are available for validating models, and in this study, ROC and AUC were utilized to verify the accuracy of landslide susceptibility maps (Azarafza et al, 2021;Shoaib et al, 2022;Zhao et al, 2022). These maps display the trade-off between sensitivity and specificity.…”

Section: Accuracy Assessment Of the Modelsmentioning

confidence: 99%

Improving landslide susceptibility prediction through ensemble Recursive Feature Elimination and meta-learning framework

Halder,

Srivast,

Ghosh

et al. 2024

Preprint

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Landslides pose significant threats to local ecological environments, causing loss of life and economic damage. This research focuses on enhancing landslide susceptibility prediction in West Bengal's Sub-Himalayan region using an innovative ensemble Recursive Feature Elimination (RFE) and meta-learning framework. Seven advanced machine learning models- Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extremely Randomized Trees (ExtraTrees), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Meta Classifier (MC) - were utilized alongside Remote Sensing and Geographic Information System techniques. Through feature selection, the ensemble identifies the most significant landslide conditioning factors. Evaluation metrics, including accuracy and AUC of the ROC curve, demonstrate the ensemble's superior predictive ability. Based on the findings, models perform well with LR (AUC = 0.935), SVM (AUC = 0.972), RF (AUC = 0.983), ExtraTrees (AUC = 0.985), GB (AUC = 0.987), and XGBoost (AUC = 0.987). However, the MC performed better than individual models with AUC = 0.987. The study's implications for land-use planning and disaster management in the region are discussed and by establishing a new benchmark for landslide susceptibility mapping, this research offers a promising approach for addressing similar environmental challenges worldwide, facilitating informed decision-making and mitigation efforts in geologically sensitive areas.

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“…Tent mapping [20] is also known as tent mapping because of the function image's resemblance to the tent shape. This is shown in Figure 3.…”

Section: Chaos Initialization and Parameter Optimizationmentioning

confidence: 99%

Enhanced Sea Horse Optimization Algorithm for Hyperparameter Optimization of Agricultural Image Recognition

Li,

Qu,

et al. 2024

Mathematics

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Deep learning technology has made significant progress in agricultural image recognition tasks, but the parameter adjustment of deep models usually requires a lot of manual intervention, which is time-consuming and inefficient. To solve this challenge, this paper proposes an adaptive parameter tuning strategy that combines sine–cosine algorithm with Tent chaotic mapping to enhance sea horse optimization, which improves the search ability and convergence stability of standard sea horse optimization algorithm (SHO). Through adaptive optimization, this paper determines the best parameter configuration in ResNet-50 neural network and optimizes the model performance. The improved ESHO algorithm shows superior optimization effects than other algorithms in various performance indicators. The improved model achieves 96.7% accuracy in the corn disease image recognition task, and 96.4% accuracy in the jade fungus image recognition task. These results show that ESHO can not only effectively improve the accuracy of agricultural image recognition, but also reduce the need for manual parameter adjustment.

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Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Cited by 12 publications

References 91 publications

Landslide susceptibility, ensemble machine learning, and accuracy methods in the southern Sinai Peninsula, Egypt: Assessment and Mapping

Landslide susceptibility, ensemble machine learning, and accuracy methods in the southern Sinai Peninsula, Egypt: Assessment and Mapping

Improving landslide susceptibility prediction through ensemble Recursive Feature Elimination and meta-learning framework

Enhanced Sea Horse Optimization Algorithm for Hyperparameter Optimization of Agricultural Image Recognition

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