Monitoring of rainfall-induced landslides at Songmao and Lushan, Taiwan, using IoT and big data-based monitoring system

Yuen-Fui, Lau,; Wang, Kuo-Lung; Wang, Y. H.; Yiu, W. H.; Ooi, G. H.; Tan, Pin Siang; Wu, Jianyu; Leung, M. L.; Lui, H. L.; Chen, C. W.

doi:10.1007/s10346-022-01964-x

Cited by 11 publications

(1 citation statement)

References 45 publications

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“…Some typical examples are fire detection in forests [3], dam level monitoring [4], quality assessment on rivers [5], rainfall [6], and particulate matter [7]. In addition, some mission-critical IoT networks are used for risk management, for example, landslides or floods [8], where continuous operation of the ENs is mandatory to save the lives of communities. Such IoT applications demand that the ENs and deployed networks are energy-efficient and reliable (i.e., the Packet Delivery Rate (PDR) is sufficiently high).…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Assisted Transmission Power Control for LoRaWAN Considering Environments With High Signal- to -Noise Variation

González-Palacio,

Tobón-Vallejo,

Sepúlveda-Cano

et al. 2024

IEEE Access

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To achieve an adequate tradeoff between range and energy efficiency, LoRaWAN End Nodes (ENs) choose their transmission parameters using an Adaptive Data Rate (ADR) scheme based on the maximum value of previous Signal-to-Noise (SNR) values. However, the ADR only performs well in favorable channel conditions. In fact, if the SNR exhibits high variability, these parameters could be inefficiently set and may negatively affect the Packet Delivery Rate (PDR). Therefore, a link margin could be overestimated to improve the PDR by the ADR algorithm, which may, however, waste the EN's energy. This paper proposes a novel ADR that does not rely on the past SNR values. Still, our proposed design directly predicts the current SNR and transmission parameters using Machine Learning. Specifically, the underlying Machine Learning models were trained using in-field measurements for six months in Medellín, Colombia, including different environmental variables and their effects on the SNR. Our ADR scheme improved energy consumption by 47.1% with a PDR of 99% and reduced collisions in dense networks up to 9.5% compared with the ADR scheme. Furthermore, we show that our proposed design outperforms some enhanced versions of the ADR scheme proposed in the literature in both energy consumption and collision rate. Finally, our proposed framework enables simple implementation since it runs directly in the ENs, improving the response time compared with the traditional ADR scheme.

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Section: Introductionmentioning

confidence: 99%

Machine-Learning-Assisted Transmission Power Control for LoRaWAN Considering Environments With High Signal- to -Noise Variation

González-Palacio,

Tobón-Vallejo,

Sepúlveda-Cano

et al. 2024

IEEE Access

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The Integrated Landslides Monitoring System of Gimigliano Municipality, Southern Italy

Capparelli,

Artese,

Carri

et al. 2024

Progress in Landslide Research and Technology

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Monitoring the stability of a slope is one of the non-structural measures aimed at reducing the risk of landslides. Displacement detection is now possible through numerous monitoring techniques, including remote sensing and ground-based solutions. In particular, in-situ monitoring allows some advantages related to using low-cost instruments whose communication can be facilitated by IoT technologies. In this chapter, we illustrate an example of an intelligent system for the integrated monitoring of the main landslide bodies of Gimigliano (CZ), southern Italy.The station includes clusters for monitoring deep movements and piezometric levels, as well as for urban structures through specific sensors and a network of sensors for topographic surface monitoring. The system was designed to be almost fully automatic and oriented to support near real-time warning activities. The data recorded by the deep and surface monitoring instruments confirm that the study area is affected by complex phenomena requiring long-term on-site monitoring.Specifically, analysis of the deep movements revealed some critical events during spring 2022 and summer 2023 that resulted in positive and negative millimetric deformations, measured by the tilt meters installed in correspondence with the monitored sites. Surface topographic analysis indicates displacement rates of 2.5–5 cm/year.

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Positions of rear scarps in retrogressive shallow soil landslide triggered by water weakening

Rao,

Chen,

Yang

et al. 2023

Bull Eng Geol Environ

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Monitoring of rainfall-induced landslides at Songmao and Lushan, Taiwan, using IoT and big data-based monitoring system

Cited by 11 publications

References 45 publications

Machine-Learning-Assisted Transmission Power Control for LoRaWAN Considering Environments With High Signal- to -Noise Variation

Machine-Learning-Assisted Transmission Power Control for LoRaWAN Considering Environments With High Signal- to -Noise Variation

The Integrated Landslides Monitoring System of Gimigliano Municipality, Southern Italy

Positions of rear scarps in retrogressive shallow soil landslide triggered by water weakening

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