Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Great Xi’an Region, China

Liu, Xiaokang; Shao, Shuai; Shao, Shengjun

doi:10.1038/s41598-024-53630-y

Cited by 3 publications

(2 citation statements)

References 70 publications

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“…China is one of the countries most severely affected by geological disasters, with landslides accounting for a significant proportion. Among the main triggers of landslides, rainfall is a highly active and variable natural factor, with approximately 90% of landslides being triggered by rainfall 2 – 4 .…”

Section: Introductionmentioning

confidence: 99%

Research on machine learning forecasting and early warning model for rainfall-induced landslides in Yunnan province

Kang,

Wan,

Gao

et al. 2024

Sci Rep

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Landslides are highly destructive geological disasters that pose a serious threat to the safety of people’s lives and property. In this study, historical records of landslides in Yunnan Province, along with eight underlying factors of landslide (elevation, slope, aspect, lithology, land cover type, normalized difference vegetation index (NDVI), soil type, and average annual precipitation (AAP)), as well as historical rainfall and current rainfall data were utilized. Firstly, we analyzed the sensitivity of each underlying factor in the study area using the frequency ratio (FR) method and obtained a landslide susceptibility map (LSM). Then, we constructed a regional rainfall-induced landslides (RIL) probability forecasting model based on machine learning (ML) algorithms and divided warning levels. In order to construct a better RIL prediction model and explore the effects of different ML algorithms and input values of the underlying factor on the model, we compared five ML classification algorithms: extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector machine (SVM), logistic regression (LR), and random forest (RF) algorithms and three representatives of the input values of the underlying factors. The results show that among the obtained forecasting models, the LSM-based RF model performs the best, with an accuracy (ACC) of 0.906, an area under the curve (AUC) of 0.954, a probability of detection (POD) of 0.96 in the test set, and a prediction accuracy of 0.8 in the validation set. Therefore, we recommend using RF-LSM model as the RIL forecasting model for Yunnan Province and dividing warning levels.

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Section: Introductionmentioning

confidence: 99%

Research on machine learning forecasting and early warning model for rainfall-induced landslides in Yunnan province

Kang,

Wan,

Gao

et al. 2024

Sci Rep

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“…to address the business issue of this research. AHP is considered as an ideal solution because it connects the gap produced by the absence of formal frameworks for decision-making, including different perspectives about the importance of a project while also bringing clarity to the prioritization process.The Analytic Hierarchy Process is a decision-making framework that breaks down complex problems into smaller components which combine quantitative and qualitative factors by quantifying and transforming opinions and judgements into quantifiable results(Liu et al, 2024). The AHP framework involves the process of converting the decision-making problems into a hierarchy of criteria and sub criterias and performing pairwise comparison analysis to define the relative importance of those elements(Abdullah & Azmael, 2023).…”

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confidence: 99%

Application of the Analytical Hierarchy Process (AHP) for Innovative Technological Projects Evaluation and Prioritization in an FMCG Company

Claus,

Aldianto

2024

ijcsrr

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The FMCG industry is known for its dynamic and competitive nature. To thrive in this business sector, companies must possess the ability to swiftly adapt to the market demand changes, continuously enhance operational efficiency and drive innovation. Those abilities are crucial for maintaining competitiveness and ensuring long term viability of the company. Over the past few decades, technology along with its advancement has emerged as a factor that disrupts the ecosystem of various industries by reshaping the way businesses operate and interact with the customers. In Indonesia, many organizations including FMCG companies have continuously embraced and adopted emerging and innovative technologies within their business operations. Although it offers various benefits for the companies, the execution process has usually encountered various challenges which causing the implementation projects to experiencing delays, especially during the decision-making process. This was also the case in one of the largest FMCG companies in Indonesia due to varying interpretations of project importance, as well as the absence of clear prioritization criteria and an unorganized decision-making process. In order to address the issues, the organization plans to develop a decision-making framework that harmonizes diverse stakeholder perspectives related to project importance based on a number of key criteria while also analyze the benefit and impact provided by the implemented technologies through the application of the Multi Criteria Decision Making (MCDM) approach using the Analytical Hierarchy Process (AHP) framework. There are three technology innovation projects to be analyzed and assessed in this research: AGV Implementation, and Digital Warehouse Management System, and Universal QR for Traceability. In order to evaluate the projects, eighteen criterion which are divided into eighteen sub criteria that has been established through Secondary Data Collection and Focus Group Discussion (FGD) process. The data were processed by leveraging the systematic decision-making structure provided by the AHP framework. The research yields two primary outcomes: a structured decision-making framework and a project prioritization scheme intended for application at the organization. The findings of the research highlight the critical role of structured decision-making in navigating the complexities of evaluating and prioritizing innovative technological projects, while also proposing a scalable model that can be repeatedly utilized by the company in the project evaluation and prioritization contexts.

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Comparison of informative modelling and machine learning methods in landslide vulnerability evaluation – a case study of Wenchuan County, China

Chen,

Li,

Zhao

et al. 2024

Geocarto International

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Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Great Xi’an Region, China

Cited by 3 publications

References 70 publications

Research on machine learning forecasting and early warning model for rainfall-induced landslides in Yunnan province

Research on machine learning forecasting and early warning model for rainfall-induced landslides in Yunnan province

Application of the Analytical Hierarchy Process (AHP) for Innovative Technological Projects Evaluation and Prioritization in an FMCG Company

Comparison of informative modelling and machine learning methods in landslide vulnerability evaluation – a case study of Wenchuan County, China

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