Performance of Slopes During Earthquake and the Following Rainfall

Xu, Jiawei; Ueda, Kyohei; Uzuoka, Ryosuke

doi:10.1007/978-3-031-11898-2_121

Cited by 1 publication

(1 citation statement)

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“…Its high seismic activity is due to its geographical location in the IndusSanpo-sutur zone, where the Eurasian and Asian plates interact [59] . The geology and seismology of Pakistan are also affected by many firstorder plate tectonic processes, including subduction zones, ongoing large-scale transform faulting, crustal compression, and extension [60]. Climatic data for the Himalayan region are scarce and the region overall has far too few weather stations because of the difficulties presented by extreme variations in altitude and aspect.…”

Section: Area Studiedmentioning

confidence: 99%

Impact of climatic anomalies and reservoir induced seismicity on earthquake generation using Federated Learning

Tehseen,

Uzma Omer,

Maham Mehr Awan

et al. 2024

VFAST trans. softw. eng.

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In this article, impact of climatic anomalies and artificial hydraulic loading on earthquake generation has been studied using federated learning (FL) technique and a model for the prediction of earthquake has been proposed. Federated Learning being one of the most recent techniques of machine learning (ML) guarantees that the proposed model possesses the intrinsic ability to handle all concerns related to data involving data privacy, data availability, data security, and network latency glitches involved in earthquake prediction by restricting data transmission to the network during different stages of model training. The main objective of this study is to determine the impact of artificial stresses and climatic anomalies on increase and decrease in regional seismicity. Experimental verification of proposed model has been carried out within 100 km radial area from 34.708o N, 72.5478o E in Western Himalayan region. Regional data of atmospheric temperature, air pressure, rainfall, water level of reservoir and seismicity has been collected on hourly bases from 1985 till 2022. In this research, four client stations at different points within the selected area have been established to train local models by calculating time lag correlation between multiple data parameters. These local models are transmitted to central server where global model is trained for generating earthquake alert with ten days lead time alarming a specific client that reported high correlation among all selected parameters about expected earthquake.

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Section: Area Studiedmentioning

confidence: 99%