Prediction of earthquake magnitude using soft computing techniques: ANN and ANFIS

Pandit, Amiya; Panda, Saleema

doi:10.1007/s12517-021-07594-2

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…We illustrate a small NN in Figure [26] as an example. As illustrated in Figure [27], we created three different neural network topologies for our investigation, each with five layers. Twelve neurons are present in the dense input layer, which is the top layer.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The neural network architecture for ANN-1 is the same as that in Figure [27]. After defining all five layers of the neural network, we constructed it using the 'RMSprop' optimizer, 'Mean Absolute Error' as the loss function, and 'Mean Squared Error' as the evaluation metric.…”

Section: Ann-1mentioning

confidence: 99%

“…We employed the same neural network architecture for ANN-2 as in Figure [27]. Using the "Adam" optimizer, "Mean Absolute Error" as the loss function, and "Mean Squared Error" as the evaluation metric, we constructed the neural network after specifying each of its five layers.…”

Section: Ann-2mentioning

confidence: 99%

“…For ANN-3, we used the same neural network architecture as shown in Figure [27]. After defining all five layers of the neural network, we compiled it using the 'SGD' optimizer, 'Mean Absolute Error' as the loss function, and 'Mean Squared…”

Section: Ann-3mentioning

confidence: 99%

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Prediction of earthquake magnitude and seismic vulnerability mapping using artificial intelligence techniques: a case study of Turkey

Biswas

Kumar

Bera

2023

Preprint

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Earthquake threats can result in fatalities, property destruction, and other cascading effects. Since it is nearly impossible to prevent earthquakes, anticipating the location of future earthquakes and figuring out their likelihood could be very helpful in reducing the seismic threat. In this work, seismic hazard prediction is executed to forecast adverse results using a range of potential artificial intelligence (AI) techniques, including ML and ANN. In the case study, we have looked at Turkey, which was recently and badly damaged by two earthquakes in February 2023. To predict earthquake magnitude, this study used a variety of regression algorithms, including Decision Tree Regressor, Extra-Trees Regressor, Random Forest Regressor, Bayesian Ridge Regressor, and advanced gradient boosting decision tree (GBDT) algorithms such as XGBoost, LightGBM, and CatBoost, as well as three artificial neural networks (ANN). The predicted magnitude and risk zone of an earthquake are mapped using a geographic information system (GIS), and the maps performed well in terms of prediction. The generated maps is showing the expected earthquake risk based on historical data using the statistical computations. The ANN models perform exceptionally well, with R2 scores of 0.99 and 0.98 for training and case study data, respectively, and low values for MSE, MAE, and RMSE. ML models have demonstrated an exceptional ability to properly generalize from a single dataset, which implies they can accurately anticipates results for new and untested data. The results would be helpful to many local emergency preparedness and infrastructure planning organizations.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Ann-1mentioning

confidence: 99%

Section: Ann-2mentioning

confidence: 99%

Section: Ann-3mentioning

confidence: 99%

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Prediction of earthquake magnitude and seismic vulnerability mapping using artificial intelligence techniques: a case study of Turkey

Biswas

Kumar

Bera

2023

Preprint

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“…23 Most of the current research related to the thermal and humid comfort of clothing has focused on the types of fabrics 24,25 and the lightweight design of synthetic materials. 26,27 To carry out exercise training and human body recovery in an effective way, 28 there are also a few studies that use cooling schemes to cool down. 29,30 The research on improving the comfort of sportswear and having a positive impact on sports efficiency by optimizing style structure and improving fabric performance is not mentioned.…”

mentioning

confidence: 99%

Optimization design of functional running pants and evaluation of thermal-humid comfort capabilities

Guo,

Li,

Chen

et al. 2023

Textile Research Journal

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In this study, we optimized the design of functional running pants and innovated a stereoscopic seaming technology. The purpose of the study was to explore the thermal and humid comfort of the athletes during running by assessing the changes in skin temperature, surface temperature, and skin humidity of a subject during running. The results of the study suggest that during continuous incremental running speed exercise and sweating heavily, the surface temperature of the running pants and the skin temperature of the human body gradually approached before running, and remained basically in equilibrium. At 40 min, the skin humidity of a subject decreased significantly, and the sweat accumulated by the human body was absorbed and evaporated. The running pants in this study still exhibit better thermal conductivity and quick-drying properties as compared to the moisture-wicking and quick-drying running pants on the market. In addition, the running pants provide designed comfort that meets the needs of the exercising human body. They effectively address the problems such as abrupt lines, rubbing against the skin, and unsuited pattern. The application of moisture-absorbing and quick-drying fabric and the innovative 0.2 cm stereoscopic seaming technology creates pores between the separation line and the human body. This allows for effective airflow throughout the pants, promoting the evaporation of sweat from the legs, which can aid athletic performance to some extent. The results of the study confirm that the optimized running pants significantly improve human body comfort and offer practical advantages in terms of athletic performance.

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Research on winter sportswear comfort and its visual model

et al. 2022

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Prediction of earthquake magnitude using soft computing techniques: ANN and ANFIS

Cited by 9 publications

References 15 publications

Prediction of earthquake magnitude and seismic vulnerability mapping using artificial intelligence techniques: a case study of Turkey

Prediction of earthquake magnitude and seismic vulnerability mapping using artificial intelligence techniques: a case study of Turkey

Optimization design of functional running pants and evaluation of thermal-humid comfort capabilities

Research on winter sportswear comfort and its visual model

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