Radon signals in soil gas associated with earthquake occurrence in Greece: review and perspective DOI
S. Stoulos, Eleftheria Papadimitriou,

V. Karakostas

et al.

Journal of Radioanalytical and Nuclear Chemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 13, 2024

Language: Английский

Application of radon (222Rn) as an environmental tracer in hydrogeological and geological investigations: An overview DOI

S. Sukanya,

Noble Jacob, Sabu Joseph

et al.

Chemosphere, Journal Year: 2022, Volume and Issue: 303, P. 135141 - 135141

Published: May 31, 2022

Language: Английский

Citations

81
Earthquake precursors: A review of key factors influencing radon concentration DOI
Pei Huang, Wenjie Lv,

Rengui Huang

et al.

Journal of Environmental Radioactivity, Journal Year: 2023, Volume and Issue: 271, P. 107310 - 107310

Published: Oct. 25, 2023

Language: Английский

Citations

17
Anomaly Detection Using Machine Learning in Hydrochemical Data From Hot Springs: Implications for Earthquake Prediction DOI Creative Commons
Ruijie Zhu, Fengtian Yang, Xiaocheng Zhou

et al.

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(6)

Published: June 1, 2024

Abstract This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. Six springs, located within an active fault zone along southeastern coast China, were carefully chosen as hydrochemical monitoring sites extended period two and a half years. Using this data, prediction model integrating six was developed to forecast M ≥ 5 earthquakes in Taiwan. The model's performance validated against recorded events, factors influencing its predictive capability analyzed. Our comprehensive analysis conclusively demonstrates superiority over traditional statistical methods prediction. Additionally, including sampling time sets significantly improves performance. However, it is important note that varies across different spring indicators type, highlighting importance identifying optimal specific scenarios. parameters, detection rate (P) response threshold (M), impact capabilities. Therefore, adjustments are needed optimize practical use. Despite limitations such inability differentiate pre‐earthquake anomalies post‐earthquake pinpoint precise location earthquakes, successfully showcases paving way further research improved methods.

Language: Английский

Citations

4
Non-linear analysis of soil 222Rn time series recorded at Jiaosi in north-east Taiwan for possible earthquake precursor study DOI
Saheli Chowdhury, Vivek Walia,

Shih-Jung Lin

et al.

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(3)

Published: Jan. 20, 2025

Language: Английский

Citations

0
Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks DOI

Fatemeh Rezaiezadeh Roukerd,

Mohammad Mahdi Rajabi

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(8)

Published: July 4, 2024

Language: Английский

Citations

3
An improved reconstruction method of the reflected dynamic pressure in shock tube system based on inverse sensing model identification DOI
Zhenjian Yao, Yongsheng Li, Bo Shi

et al.

Aerospace Science and Technology, Journal Year: 2024, Volume and Issue: 145, P. 108903 - 108903

Published: Jan. 21, 2024

Language: Английский

Citations

2
Transformer-based enhanced model for accurate prediction and comprehensive analysis of hazardous waste generation in Shanghai: Implications for sustainable waste management strategies DOI

Wenjie Shi,

Youcai Zhao,

Zongsheng Li

et al.

Chemosphere, Journal Year: 2023, Volume and Issue: 338, P. 139579 - 139579

Published: July 18, 2023

Language: Английский

Citations

4
Time-dependent variations of groundwater radon: Insights from a twelve-year study in the Baikal region, East Siberia, Russia DOI
A. K. Seminsky, К. Zh. Seminsky

Journal of Environmental Radioactivity, Journal Year: 2024, Volume and Issue: 278, P. 107509 - 107509

Published: July 30, 2024

Citations

1
Anomalies in Infrared Outgoing Longwave Radiation Data before the Yangbi Ms6.4 and Luding Ms6.8 Earthquakes Based on Time Series Forecasting Models DOI Creative Commons

Junqing Zhu,

Ke Sun, Jingye Zhang

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(15), P. 8572 - 8572

Published: July 25, 2023

Numerous scholars have used traditional thermal anomaly extraction methods and time series prediction models to study seismic anomalies based on longwave infrared radiation data. This paper selected bidirectional long short-term memory (BILSTM) as the research algorithm after analyzing comparing performance of five models. Based outgoing (OLR) data, model was predict values in spatial area 5° × at epicenter for 30 days before earthquake. The confidence interval evaluation criterion extract anomalies. examples earthquakes were Yangbi Ms6.4-magnitude earthquake Yunnan 21 May 2021 Luding Ms6.8-magnitude Sichuan 5 September 2022. results showed that observed 15 16 (5 6 May) exceeded over a wide large extent. indicates strong concentrated OLR observations 27 (9 August), 18 (18 8 (28 August) local by extent, indicating scattered Overall, method this extracts both temporal dimensions is an effective extracting

Language: Английский

Citations

3
Time-series analysis of radon monitoring in soil gas in association with earthquakes in Stivos faulting, at Lagadas basin, North Greece DOI Creative Commons
S. Stoulos,

Ioannidou Alexandra

Journal of Radioanalytical and Nuclear Chemistry, Journal Year: 2023, Volume and Issue: 332(11), P. 4581 - 4590

Published: Oct. 6, 2023

Abstract Time series analysis was applied to the continuous radon level, temperature, pressure, and rainfall find clear earthquake signals. Radon signals appeared a few days after heavy rains, associated with events M = 3.8–4.2 were detected 12 up 36 before. The are complete data recorded from 1983 1986, giving discussion conclusion on prediction time anomaly in Stivos faulting near Thessaloniki, N. Greece.

Language: Английский

Citations

3