A comparative analysis of weight-based machine learning methods for landslide susceptibility mapping in Ha Giang area

Trinh, Thanh; Luu, Binh Thanh; Le, Trang Ha Thi; Nguyen, Duong Huy; Tran, Trong Van; Nguyen, Thi Hai Van; Nguyen, Kristy; Nguyen, Lien T.

doi:10.1080/20964471.2022.2043520

Cited by 18 publications

(5 citation statements)

References 63 publications

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“…Researchers worldwide have used many methods (including Physically-based, MCDM, Statistical, Machine Learning, and Deep Learning) and techniques (including Ground-based field study, Complex Mechanical and Engineering, Remote Sensing, and GIS) to create accurate landslide prediction models, but this topic produced disagreement among experts (Jennifer, 2022;Kainthura and Sharma, 2023;Trinh et al, 2022;Addis, 2023;Jam et al, 2023;Khaliq et al, 2023;Konurhan et al, 2023). Consequently, it is necessary to devise and use inventive approaches for constructing and executing landslide prediction models.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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show abstract

“…Afterwards, the consistency ratio is computed based on Table 1. Source: (Trinh et al, 2022) The Fuzzy Analytic Hierarchy Process (FAHP) method is a development of the AHP method combined with fuzzy set theory. In the FAHP method, a fuzzy ratio scale is used to indicate the relative influence of related factors, whether they can be directly measured or not.…”

Section: Figure 1 Sustainable Green Data Center Frameworkmentioning

confidence: 99%

Enabling Practical Decision Making For Sustainable Green Data Center Planning

Muhamad Faris Naufal Austen,

Athor Subroto

2023

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Data centers play a crucial role in storing and processing data in today's digital age, leading to a surge in demand for sustainable green data center planning. However, implementing practical measures to achieve sustainability remains a challenge for data center managers. This study aims to aid their informed decision-making in sustainable green data center planning. Previous research has identified seven green data center key components: ICT governance, infrastructure, energy, equipment lifecycle, green technology, benchmarking, and business continuity. Subsequently, the study expanded by utilizing the FAHP method to evaluate the perspectives of various experienced data center. Those green data center components were evaluated against each other regarding the three sustainability criteria: environment, economy, and corporate. Consequently, it was discovered that infrastructure, green technology, and business continuity consistently held the highest fuzzy weight in multiple sensitivity analysis scenarios. Thus, data center managers can allocate resources based on priority rankings and adjust accordingly.

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“…Nguyen et al [21] used RF models to predict the Normalized Difference Vegetation Index values to study the potential impact of climate change on vegetation growth. Trinh et al [22] used Logistic regression, SVM, and RF to generate landslide susceptibility mapping in the Ha Giang province of Vietnam. Eltazarov et al [23] used RF to downscale gridded precipitation data and then investigate the risk reduction potential of weather index insurance.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Temperature Evolution Maps of Bangladesh via Data-Driven Learning

Wu,

Yang,

Zhang

et al. 2024

Atmosphere

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As a developing country with an agricultural economy as a pillar, Bangladesh is highly vulnerable to adverse effects of climate change, so the generation of high-resolution temperature maps is of great value for Bangladesh to achieve agricultural sustainable development. However, Bangladesh’s weak economy and sparse meteorological stations make it difficult to obtain such maps. In this study, by mining internal features and links inside observed data, we developed an efficient data-driven downscaling technique to generate high spatial-resolution temperature distribution maps of Bangladesh directly from observed temperature data at 34 meteorological stations with irregular distribution. Based on these high-resolution historical temperature maps, we further explored a data-driven forecast technique to generate high-resolution temperature maps of Bangladesh for the period 2025–2035. Since the proposed techniques are very low-cost and fully mine internal links inside irregular-distributed observations, they can support relevant departments of Bangladesh to formulate policies to mitigate and adapt to climate change in a timely manner.

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A comparative analysis of weight-based machine learning methods for landslide susceptibility mapping in Ha Giang area

Cited by 18 publications

References 63 publications

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

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

Enabling Practical Decision Making For Sustainable Green Data Center Planning

High-Resolution Temperature Evolution Maps of Bangladesh via Data-Driven Learning

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