Earthquake Early Warning (EEW) System: System Architecture, Data Modelling, and User Interface Design

Noprisson, Handrie

doi:10.32628/cseit2173136

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Untuk memastikan keamanan transportasi kereta api, beberapa riset telah telah dilakukan untuk mengembangkan sistem kereta api terintegrasi. Melalui model pembelajaran berbasis kecerdasan buatan, sistem ini mencoba mendeteksi wilayah rel kereta api yang akan diamati [11]- [21][22]- [24]. Penelitian ini merupakan bagian dari pengembangan sistem tersebut yang bertujuan untuk deteksi kerusakan jalur rel kereta api dengan menggunakan model CLAHE-KNN.…”

Section: Pendahuluanunclassified

Railroad Track Damage Detection Using CLAHE-KNN (CLAHE K-Nearest Neighbor)

Noprisson¹,

Ayumi²

2023

JSAI

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This research is part of the development of the system, which aims to detect damage to railway tracks using the CLAHE-KNN model. This study aims to detect railway damage using the CLAHE-K-NN method and determine the effect of CLAHE on the K-Nearest Neighbor algorithm for railway damage detection. CLAHE is defined by Block Size (BS) and Clip Limit (CL). The dataset used in this study was railway imagery totaling 384 data. Based on the KNN method's experimental results, this method obtained an accuracy of 65.62%. In addition, the experimental results using KNN with CLAHE optimization get better accuracy by using clipLimit = 4.0 and tileGridSize = (4.8), which is 66.67%.

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Section: Pendahuluanunclassified

Railroad Track Damage Detection Using CLAHE-KNN (CLAHE K-Nearest Neighbor)

Noprisson¹,

Ayumi²

2023

JSAI

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An Evolutionary Gravitational Neocognitron Neural Network for Earthquake Parameters Observation in IoT System-Based Earthquake Early Warning

Manikannan,

Bansod,

Murugan

et al. 2024

J. Earthquake and Tsunami

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An earthquake early-warning system (EEWS) is essential for preserving human life. In terms of disaster management and EQ risk reduction, quickly determining the earthquake’s (EQ’s) magnitude and position is important. To prevent an EQ catastrophe, these parameters can be transmitted over an IoT network. The Evolutionary Gravitational Neocognitron Neural Network (EGNNN), a novel technique for real-time earthquake parameter observation, is introduced in this paper to enhance earthquake early warning (EEW) systems. The proposed framework uses various sophisticated data processing methods to analyze publically accessible earthquake data from sources like the Japan Meteorological Agency. The Morphological Filtering Extended Empirical Wavelet Transformation (MFEEWT) method is first used to pre-process seismic waveform input, significantly lowering noise levels in the data. After that, from the pre-processed waveforms the relevant features are extracted via the Fast Discrete Curvelet Transform with Wrapping (FDCT-WRP) and a Stacked Autoencoder (SAE). The EGNNN, an innovative neural network model created for earthquake parameter prediction, specifically location and magnitude, uses these extracted features as input. The Osprey optimization algorithm (OOA) is employed to enhance the EGNNN model. A crucial component of EEW systems, dependable and quick alert delivery is ensured by the proposed system’s integration with an Internet of Things (IoT) framework. The EGNNN swiftly evaluates earthquake parameters and sends this vital data to a centralized IoT system, enabling pertinent entities to act promptly. Performance evaluation is conducted on the Python platform, comparing the proposed technique with existing methods. The proposed method EGNNN-EPO-IoT achieves a higher warning time of 23.34%, 3.45%, and 7.86%; 12.23%, 32.21%, and 22.09% higher accuracy compared to the other existing techniques such as 3S-AE-CNN-EPO-IoT (3 sec-Autoencoder-Convolutional neural network-Earthquake parameter observation-IoT), XGB-EPO-IoT (Extreme Gradient Boosting-EPO-IoT), and SVM-EPO-IoT (Support Vector Machine-EPO-IoT).

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Earthquake Early Warning (EEW) System: System Architecture, Data Modelling, and User Interface Design

Cited by 2 publications

References 34 publications

Railroad Track Damage Detection Using CLAHE-KNN (CLAHE K-Nearest Neighbor)

Railroad Track Damage Detection Using CLAHE-KNN (CLAHE K-Nearest Neighbor)

An Evolutionary Gravitational Neocognitron Neural Network for Earthquake Parameters Observation in IoT System-Based Earthquake Early Warning

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