Effect of different mapping units, spatial resolutions, and machine learning algorithms on landslide susceptibility mapping at the township scale

Environmental Earth Sciences, Год журнала: 2025, Номер 84(5)

Опубликована: Фев. 26, 2025

Язык: Английский

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Landslide susceptibility mapping based on the reliability of landslide and non-landslide sample

Expert Systems with Applications, Год журнала: 2023, Номер 243, С. 122933 - 122933

Опубликована: Дек. 14, 2023

Язык: Английский

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A new procedure for optimizing neural network using stochastic algorithms in predicting and assessing landslide risk in East Azerbaijan

Stochastic Environmental Research and Risk Assessment, Год журнала: 2024, Номер unknown

Опубликована: Март 21, 2024

Язык: Английский

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Exploring the decision-making process of ensemble learning algorithms in landslide susceptibility mapping: Insights from local and global explainable AI analyses

Advances in Space Research, Год журнала: 2024, Номер 74(8), С. 3765 - 3785

Опубликована: Июль 6, 2024

Язык: Английский

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Landslide hazard zonation using advanced machine learning algorithms based on statistical models (Case study: Sarvabad, Iran)

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Апрель 10, 2025

Язык: Английский

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Exploring the Effectiveness of Social Media on Tourism Destination Marketing: An Empirical Study in a Developing Country

WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS, Год журнала: 2024, Номер 21, С. 1392 - 1408

Опубликована: Май 31, 2024

More than 3.049 billion monthly active social media (Facebook) users are engaging in sharing content, views, surfing, and bridging their friends family. Web 3.0 is a buzzword now That going to ensure the decentralization of information, blockchain technology, data security, privacy, individual control over private data. The world’s scenery has been changed through invention internet landscape same way this century. Social revolutionized companies convey assortment products services prospective customers. It become catalyst for changing decisions users. enabled every little firm large conglomerate pinpoint niche customer segment reach them effectively with various techniques. observed that influencers have noteworthy robust correlation (i.e., 73%) contribute most travelers’ travel decision-making, indicating least reliability (28% correlation). study shows R square value 0.88 adjusted 0.88, decision-making 88% shaped by influencers. Therefore, boom also facilitated people communicate each other, especially when they seek recommendations purchasing something or availing service. provides platform both consumers businesses two-way communication where parties can interact on real-time basis without incurring much expense. brought lot tourist destinations closer tourists situated remote part country place quite untouched groups. allows destination organizations like sites, parks, hotels, amusement centers conveniently publish multimedia content involving blog posts, images, videos, interactive games attract visitors these destinations. marketing information profiles, behaviors, perceptions manager. influences groups making choosing destination, time travel, package might want avail of. In research paper, we identified four stimuli shown tourists’ decision-making. it more 80% time, positive influence about tour itinerary. opened horizon decentralized communications among all world. People easily find as looking meet demands.

Язык: Английский

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Settlement Site Selection Model for Multihazard Risky Areas with Open Source Web-GIS, Machine Learning, and MCDM

Journal of the Indian Society of Remote Sensing, Год журнала: 2025, Номер unknown

Опубликована: Март 15, 2025

Язык: Английский

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Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain

Geosciences, Год журнала: 2025, Номер 15(3), С. 110 - 110

Опубликована: Март 18, 2025

Flooding is one of the most significant natural hazards in Iran, primarily due to country’s arid and semi-arid climate, irregular rainfall patterns, substantial changes watershed conditions. These factors combine make floods a frequent cause disasters. In this case study, flood susceptibility patterns Marand Plain, located East Azerbaijan Province northwest were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Forest (RF), Bagging, Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, geographical affecting at 485 identified flood-prone points. data geographic information system, with dataset divided into 70% for training 30% testing build validate models. An gain ratio multicollinearity analysis employed assess influence various on occurrence, flood-related variables classified quantile classification. frequency method was used evaluate significance each factor. Model performance evaluated statistical measures, including Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, an area under ROC curve (AUROC) exceeding 0.90. Among models, LWL algorithm delivered accurate predictions, followed by RF, M5P, RSS. LWL-generated map 9.79% study as highly susceptible flooding, 20.73% high, 38.51% moderate, 29.23% low, 1.74% very low. findings research provide valuable insights government agencies, local authorities, policymakers designing strategies mitigate risks. This offers practical framework reducing impact future through informed decision-making risk management strategies.

Язык: Английский

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Landslide Susceptibility Assessment Using Recurrent Neural Network (RNN)—A Case of Chabahar and Konarak in Iran

Indian geotechnical journal, Год журнала: 2025, Номер unknown

Опубликована: Апрель 3, 2025

Язык: Английский

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Landslide Susceptibility Assessment Using Hybrid Method of Best-first Decision Tree and Machine Learning Ensembles

KSCE Journal of Civil Engineering, Год журнала: 2025, Номер unknown, С. 100199 - 100199

Опубликована: Апрель 1, 2025

Язык: Английский

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