An ontology‐based system for discovering landslide‐induced emergencies in electrical grid

Phengsuwan, Jedsada; Shah, Tejal; Sun, Rui; James, Philip; Thakker, Dhavalkumar; Ranjan, Rajiv

doi:10.1002/ett.3899

Cited by 3 publications

(3 citation statements)

References 43 publications

(38 reference statements)

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“…The proposed architecture in Reference 39 supports quick actions via context‐aware cognitive services for disaster mitigation curtailing the impact of forthcoming disasters. Reference 40 proposed data analytics and integration system enabling interaction between electrical grid‐based EWS (Early warning systems) and natural hazard EWS, facilitating electrical grid‐based monitoring and also provide support to decision‐makers for managing electrical grid‐based framework. Architecture implemented by Reference 40 includes historical information about landslides, along with several data sources to simplify the mechanism of data analysis and integration.…”

Section: Introductionmentioning

confidence: 99%

“…Reference 40 proposed data analytics and integration system enabling interaction between electrical grid‐based EWS (Early warning systems) and natural hazard EWS, facilitating electrical grid‐based monitoring and also provide support to decision‐makers for managing electrical grid‐based framework. Architecture implemented by Reference 40 includes historical information about landslides, along with several data sources to simplify the mechanism of data analysis and integration. By utilizing the observation modeled, a developed prototype system is capable of reporting the occurrences of landslides and also suggests capable data sources monitoring electrical grid‐based network 40 …”

Section: Introductionmentioning

confidence: 99%

“…Architecture implemented by Reference 40 includes historical information about landslides, along with several data sources to simplify the mechanism of data analysis and integration. By utilizing the observation modeled, a developed prototype system is capable of reporting the occurrences of landslides and also suggests capable data sources monitoring electrical grid‐based network 40 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Review on remote sensing methods for landslide detection using machine and deep learning

Mohan

Singh

Kumar

et al. 2020

Trans Emerging Tel Tech

169

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Landslide, one of the most critical natural hazards, is caused due to specific compositional slope movement. In the past decades, due to inflation of urbanized area and climate change, a compelling expansion in landslide prevalence took place which is also termed as mass/slope movement and mass wasting, causing extensive collapse around the world. The principal reason for its pursuance is a reduction in the internal resistance of soil and rocks, classified as a slide, topple, fall, and flow. Slopes can be differentiated based on earth material and the nature of its movements. The downward flow of landslides occurs due to excessive rainfall, snowmelt, earthquake, volcanic eruption, and so on. This review article revisits the conventional approaches for identification of landslides, predicting future risk, associated with slope failures, followed by emphasizing the advantages of modern geospatial techniques such as aerial photogrammetry, satellite remote sensing images (ie, panchromatic, multispectral, radar images), Terrestrial laser scanning, and High‐Resolution Digital Elevation Model (HR‐DEM) in updating landslide inventory maps. Machine learning techniques like Support Vector Machine, Artificial neural network, deep learning has been extensively used with geographical data producing effective results for assessment of natural hazard/resources and environmental research. Based on recent studies, deep learning is a reliable tool addressing remote sensing challenges such as trade‐off in imaging system producing poor quality investigation, in addition, to expedite consequent task such as image recognition, object detection, classification, and so on. Conventional methods, like pixel and object‐based machine learning methods, have been broadly explored. Advanced development in deep learning technique like CNN (Convolutional neural network) has been extensively successful in information extraction from an image and has exceeded other traditional approaches. Over the past few years, minor attempts have been made for landslide susceptibility mapping using CNN. In addition, small sample sizes for training purpose will be major drawback and notably remarkable while using deep learning techniques. Also, assessment of the model's performance with diverse training and testing proportion other than commonly utilized ratio, that is, 70/30 needs to be explored further. The review article briefly highlights the remote sensing methods for landslide detection using machine learning and deep learning.

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Section: Introductionmentioning

confidence: 99%