Spatio–temporal attention based real-time environmental monitoring systems for landslide monitoring and prediction

Babu, M. Hari; Ashokkumar, N.; Joshi, Anjali; Deshpande, Pallavi S.; Keshta, Ismail; Maaliw, Renato R.

doi:10.1007/s41324-023-00532-2

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…In reality, many important datasets exist as graphs or networks. Thus, combining a neural network with graph analysis could resolve numerous problems [52]. A graph, also known as a topology, is an important concept in computer science.…”

Section: Vision Graph Neural Network (Vig)mentioning

confidence: 99%

A Strategy for Neighboring Pixel Collaboration in Landslide Susceptibility Prediction

Wang,

Zhang

et al. 2024

Remote Sensing

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Landslide susceptibility prediction usually involves the comprehensive analysis of terrain and other factors that may be distributed with spatial patterns. Without considering the spatial correlation and mutual influence between pixels, conventional prediction methods often focus only on information from individual pixels. To address this issue, the present study proposes a new strategy for neighboring pixel collaboration based on the Unified Perceptual Parsing Network (UPerNet), the Vision Transformer (ViT), and Vision Graph Neural Networks (ViG). This strategy efficiently utilizes the strengths of deep learning in feature extraction, sequence modeling, and graph data processing. By considering the information from neighboring pixels, this strategy can more accurately identify susceptible areas and reduce misidentification and omissions. The experimental results suggest that the proposed strategy can predict landslide susceptibility zoning more accurately. These predictions can identify flat areas such as rivers and distinguish between areas with high and very high landslide susceptibility. Such refined zoning outcomes are significant for landslide prevention and mitigation and can help decision-makers formulate targeted response measures.

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Section: Vision Graph Neural Network (Vig)mentioning

confidence: 99%

A Strategy for Neighboring Pixel Collaboration in Landslide Susceptibility Prediction

Wang,

Zhang

et al. 2024

Remote Sensing

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Landslide monitoring and prediction system using geosensors and wireless sensor network

Chaulya,

Mishra,

Kumar

et al. 2024

Discov Geosci

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Landslides in hilly regions are a frequently occurring natural phenomenon which takes a heavy toll of human lives and causes damage to different properties. Hence, prediction of landslide is essential for averting its deleterious effects by providing early warning to neighboring residents about the impending hazard of landslide. A landslide monitoring system has been designed using geosensors and high-range wireless network for on-line monitoring and prediction of landslides with the application of multivariate statistical analysis of prevailing site parameters. The system consists of various geosensors, wireless sensor network, server, and landslide monitoring and forecasting software. Crackmeter, in-place inclinometer, raingauge, tiltmeter, piezometer and other sensors are set up in the selected hill slope prone to landslide for continuous monitoring of influencing parameters. The system measures real-time landslide parameters using the said geosensors connected with wireless nodes and establishing a dynamic wireless network to overcome redundancy issue using wireless nodes of around 1200 m communication range using a high performance low power microcontroller, integrated solar panel and additional external omni-directional antenna for monitoring landslide in a large and hazardous hilly region from a distant safer location. The application software consists of different modules, namely data monitoring, analysis, storing, viewing, prediction, and generation of audio-visual, SMS and email alerts for 3 levels of landslides situations. The paper enumerates the system architecture and the application software details. Graphical Abstract

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Design and optimization of power management circuits for low-power VLSI systems

Ramashri,

Christy,

Abdul Munaf

et al. 2023

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)

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Spatio–temporal attention based real-time environmental monitoring systems for landslide monitoring and prediction

Cited by 3 publications

References 29 publications

A Strategy for Neighboring Pixel Collaboration in Landslide Susceptibility Prediction

A Strategy for Neighboring Pixel Collaboration in Landslide Susceptibility Prediction

Landslide monitoring and prediction system using geosensors and wireless sensor network

Design and optimization of power management circuits for low-power VLSI systems

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