Emerging technologies and supporting tools for earthquake disaster management: A perspective, challenges, and future directions

Progress in Disaster Science, Journal Year: 2024, Volume and Issue: 23, P. 100347 - 100347

Published: July 3, 2024

Seismology is among the ancient sciences that concentrate on earthquake disaster management (EQDM), which directly impact human life and infrastructure resilience. Such a pivot has made use of contemporary technologies. Nevertheless, there need for more reliable insightful solutions to tackle daily challenges intricacies natural stakeholders must confront. To consolidate substantial endeavors in this field, we undertake comprehensive survey interconnected More particularly, analyze data communication networks (DCNs) Internet Things (IoT), are main infrastructures seismic networks. In accordance, present conventional innovative signal-processing techniques seismology. Then, shed light evolution EQ sensors including acoustic based optical fibers. Furthermore, address role remote sensing (RS), robots, drones EQDM. Afterward, highlight social media contribution. Subsequently, elucidation diverse optimization employed seismology prolonging presented. Besides, paper analyzes important functions artificial intelligence (AI) can fulfill several areas Lastly, guide how prevent disasters preserve lives.

Language: Английский

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Performance enhancement of artificial intelligence: A survey

Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: unknown, P. 104034 - 104034

Published: Sept. 1, 2024

Language: Английский

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Real-Time Earthquake Detection and Intensity Forecasting System

International Journal of Advanced Research in Science Communication and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 275 - 279

Published: April 3, 2025

Earthquakes pose a severe threat to life and infrastructure, necessitating efficient real-time detection accurate intensity forecasting. This research presents Real-Time Earthquake Detection Intensity Forecasting System that utilizes ADXL335 accelerometers Arduino Uno microcontrollers for seismic data acquisition. The system processes acceleration data, applies noise filtering, detects events based on predefined thresholds. To enhance forecasting accuracy, Long Short-Term Memory (LSTM) neural network is employed, leveraging historical patterns precise magnitude prediction. integration of sensor-based collection with deep learning improves reliability, enabling timely alerts early warnings. Experimental results demonstrate the system’s effectiveness in detecting activity high accuracy minimal false positives. contributes earthquake monitoring, offering scalable cost-effective solution warning applications

Language: Английский

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Enhancing analyst decisions for seismic source discrimination with an optimized learning model

Geoenvironmental Disasters, Journal Year: 2024, Volume and Issue: 11(1)

Published: Aug. 8, 2024

Abstract Sustainable development in urban areas requires a wide variety of current and theme-based information for efficient management planning. In addition, researching the spatial distribution earthquake (EQ) clusters is an important step reducing seismic risks EQ losses through better assessment hazards, therefore it desirable to acquire uncontaminated database activity. Quarry blasts (QBs) conducted over mapped area have tainted seismicity inventory northwestern region Egypt, which focus this paper. Separating these QBs from EQs hence preferable accurate risk assessments. Consequently, we present highly effective ML model cleaning up database, allowing delineation using data single station, “ AYT ”, part Egyptian National Seismic Network. The magnitudes $$\le 3$$ ≤ 3 that are very uncertain as or need significant amount time analyze primary model. order find best way classify QBs, method looks at number models before settling on one eight features. results show suggested method, uses quadratic discrimination analysis discriminating, successfully separates with 99.4% success rate.

Language: Английский

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Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10169 - 10169

Published: Nov. 6, 2024

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence occurrence shallow seismic events by focusing on developing an innovative concept combines historical meteorological data collection to predict potential earthquakes. A machine learning (ML) model utilizing ML.NET framework was designed implemented. An analysis undertaken identify which modeling approach, prediction, or classification performs better in forecasting events. trained a dataset 8766 records corresponding period from 1 January 2001 5 October 2024. achieved accuracy 95.65% for earthquake based conditions Vrancea region, Romania. authors proposed unique alerting algorithm conducted case evaluates multiple predictive models, varying parameters, methods effective event specific conditions. findings demonstrate combining Internet Things (IoT)-based environmental monitoring with AI improve preparedness. IoT-based application developed using C# ASP.NET enhance public warning capabilities, leveraging Azure cloud infrastructure. also created hardware prototype real-time alerting, integrating M5Stack platform ESP32 MPU-6050 sensors validation. testing phase results describe methodology various scenarios.

Language: Английский

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Mobile Platforms as the Alleged Culprit for Work–Life Imbalance: A Data-Driven Method Using Co-Occurrence Network and Explainable AI Framework

Sustainability, Journal Year: 2024, Volume and Issue: 16(18), P. 8192 - 8192

Published: Sept. 20, 2024

The digital transformation of organizations has propelled the widespread adoption mobile platforms. Extended availability and prolonged engagement with platform-mediated work have blurred boundaries, making it increasingly difficult for individuals to balance life. Criticism platforms intensified, precluding towards a sustainable future. This study examines complex relationship between work–life imbalance using comprehensive data-driven methodology. We employed co-occurrence network technique extract relevant features based on previous findings. Subsequently, we applied an explainable AI framework analyze nonlinear relationships underlying technology-induced detect behavior patterns. Our results indicate that there is threshold beneficial effects demands integration behavior. Beyond this tolerance range, no further positive increase can be observed. For aiming either constrain or foster employees’ behavior, our findings provide tailored strategies meet different needs. By extending application advanced machine learning algorithms predict behaviors, offers nuanced insights counter alleged issue imbalance. This, in turn, promotes success initiatives. significant theoretical practical implications organizational transformation.

Language: Английский

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Improving Structural Resilience in Earthquake-Prone Areas through Seismic Retrofitting Strategies

International Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: 11(11), P. 106 - 122

Published: Nov. 30, 2024

In the realm of structural engineering, seismic resilience building structures stands as a paramount concern, especially in regions prone to activity. However, absence stringent design requirements current standards has left many existing vulnerable devastating effects earthquakes. This review paper addresses this critical issue by exploring various strategies and analytical techniques for retrofitting pre-existing structures. Also, discusses shortcomings considerations need measures. It explores classification methods analysis methodologies, including software tools empirical approaches, integration artificial intelligence (AI) improve accuracy efficiency monitoring under conditions. The also examines economic aspects retrofitting, conducting comprehensive cost evaluate financial implications against potential benefits enhancing resilience. aims analyze retrofit considering technical efficacy cost-effectiveness, which are essential researchers facilitate informed decision-making proactive measures safeguard destructive forces

Language: Английский

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