Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 10, 2025

Язык: Английский

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Development and assessment of a novel hybrid machine learning-based landslide susceptibility mapping model in the Darjeeling Himalayas

Stochastic Environmental Research and Risk Assessment, Год журнала: 2023, Номер unknown

Опубликована: Авг. 4, 2023

Язык: Английский

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Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

Remote Sensing, Год журнала: 2023, Номер 15(16), С. 4098 - 4098

Опубликована: Авг. 21, 2023

Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages image processing, signal recognition, object detection, has facilitated scientific research EDA. This paper analyses 204 articles through systematic literature review to investigate the status quo, development, challenges of DL for The first examines distribution characteristics trends two categories EDA assessment objects, including earthquakes secondary disasters as buildings, infrastructure, areas physical objects. Next, this study application distribution, advantages, disadvantages three types data (remote sensing data, seismic social media data) mainly involved these studies. Furthermore, identifies six commonly used models EDA, convolutional neural network (CNN), multi-layer perceptron (MLP), recurrent (RNN), generative adversarial (GAN), transfer (TL), hybrid models. also systematically details at different times (i.e., pre-earthquake stage, during-earthquake post-earthquake multi-stage). We find that most extensive field involves using CNNs classification detect assess building damage resulting from earthquakes. Finally, discusses related training models, opportunities new sources, multimodal DL, concepts. provides valuable references scholars practitioners fields.

Язык: Английский

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Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review

Remote Sensing, Год журнала: 2024, Номер 16(16), С. 2947 - 2947

Опубликована: Авг. 12, 2024

This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution research publication trends, (3) progress of algorithms, (4) application techniques models for landslide susceptibility mapping, detections, prediction, inventory deformation monitoring, assessment, extraction management. selections were based on keyword searches using title/abstract keywords from Web Science Scopus. A total 186 articles published between 2011 2024 critically reviewed to provide answers questions related the recent advances use technologies combined with artificial intelligence (AI), machine (ML), deep (DL) algorithms. review revealed that these methods have high efficiency detection, hazard mapping. few current issues also identified discussed.

Язык: Английский

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Modeling land susceptibility to wind erosion hazards using LASSO regression and graph convolutional networks

Frontiers in Environmental Science, Год журнала: 2023, Номер 11

Опубликована: Май 9, 2023

Predicting land susceptibility to wind erosion is necessary mitigate the negative impacts of on soil fertility, ecosystems, and human health. This study first attempt model hazards through application a novel approach, graph convolutional networks (GCNs), as deep learning models with Monte Carlo dropout. approach applied Semnan Province in arid central Iran, an area vulnerable dust storms climate change. We mapped 15 potential factors controlling erosion, including climatic variables, characteristics, lithology, vegetation cover, use, digital elevation (DEM), then least absolute shrinkage selection operator (LASSO) regression discriminate most important factors. constructed predictive by randomly selecting 70% 30% pixels, training validation datasets, respectively, focusing locations severe inventory map. The current LASSO identified eight out features (four property categories, speed, evaporation) Province. These were adopted into GCN model, which estimated that 15.5%, 19.8%, 33.2%, 31.4% total characterized low, moderate, high, very high respectively. under curve (AUC) SHapley Additive exPlanations (SHAP) game theory assess performance interpretability output, AUC values for datasets at 97.2% 97.25%, indicating excellent prediction. SHAP ranged between −0.3 0.4, while analyses revealed coarse clastic component, use effective output. Our results suggest this suite methods highly recommended future spatial prediction other environments around globe.

Язык: Английский

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Understanding the scale effects of topographical variables on landslide susceptibility mapping in Sikkim Himalaya using deep learning approaches

Geocarto International, Год журнала: 2022, Номер 37(27), С. 17826 - 17852

Опубликована: Окт. 14, 2022

In geomorphological hazard studies, selecting DEM data with the proper spatial resolution is necessary for optimal analysis of prediction performance. Henceforth, accurate in landslide susceptibility study also crucial this perspective. This determines scale effects derived hydro-topographic factors LS mapping Rangpo river basin, Sikkim Himalaya, India. Five different i.e., ALOS (12.5 m), and AW3D30, SRTM, ASTER Cartosat-1 each 30 m were used study. Three neural network algorithms applied to produce LSM. The results investigation revealed that, among three employed techniques, deep learning algorithm performed best. proposed unique approach combination can be useful precise LSMs hilly areas around globe, will helpful sustainable development.

Язык: Английский

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Investigating the dynamic nature of landslide susceptibility in the Indian Himalayan region

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(3)

Опубликована: Фев. 13, 2024

Язык: Английский

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Earthquake hotspot and coldspot: Where, why and how?

Geosystems and Geoenvironment, Год журнала: 2022, Номер 2(1), С. 100130 - 100130

Опубликована: Авг. 31, 2022

Global tectonic activities are playing an important role in the occurrences of devastating earthquakes and related long-term changes earth's system surface. However, plate tectonics processes their interaction with crust very much complex, it is a subject unending debate. Therefore, tectonism-induced landslide, tsunami, liquefaction, fire significant earthquake-related hazards, which have larger potential overwhelming impact on life infrastructural properties throughout world. In this study, we emphasized identification earthquake hotspot coldspot zones considering historical data across boundary Here, total 7773 points were collected as input parameters three-moment magnitude (Mw) classes (<4.5, 4.5–6.0, >6.0). Two statistical methods namely analysis (Getis-Ord GI*) optimized used detection global using geographic information (GIS) platform. Hotspot zone identified under 99%, 95%, 90% confidence levels. Alongside, here also discussed paradigm, evidence tectonic, earthquakes, how why they formed help existing theoretical constraints. The result indicates that Pacific ring fire, Peru-Chile Trench, mid-Atlantic oceanic ridge fall

Язык: Английский

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ChatGPT in transforming communication in seismic engineering: Case studies, implications, key challenges and future directions

Earthquake Science, Год журнала: 2024, Номер 37(4), С. 352 - 367

Опубликована: Июль 13, 2024

Язык: Английский

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Machine Learning-assisted Investigation of Landslide Susceptibility for Aglar Watershed in the Lesser Himalaya Region

Journal of the Geological Society of India, Год журнала: 2025, Номер 101(3), С. 384 - 396

Опубликована: Март 1, 2025

Язык: Английский

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