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- W4320025706 abstract "The artificial neural network (ANN) model for predicting seismic tremor magnitudes for Japan is portrayed in this paper. The authors have retrieved the earthquake dataset from European-Mediterranean Seismological Center for this study. The dataset comprises a list of 5000 quake events that occurred from 1 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>st</sup> July 2010 to 14 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>th</sup> April 2016 in the region of Japan. Different neural network structures and ANN configurations exemplify the ANN model construction. The experiment is carried out by fine-tuning network variables such as type, transfer function, training function, and hidden neurons. The forecast accuracies of each of these network configurations are compared. The resultant ANN model features Levenberg-Marquardt backpropagation method for training the model, the nonlinear sigmoid activation function for the hidden layer, and the model's performance is evaluated concerning Mean Squared Error (MSE) and Gradient (g)." @default.
- W4320025706 created "2023-02-12" @default.
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- W4320025706 date "2022-12-15" @default.
- W4320025706 modified "2023-09-30" @default.
- W4320025706 title "Magnitude Prediction Model for Japan Seismic Tremors Using Artificial Neural Network" @default.
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- W4320025706 doi "https://doi.org/10.1109/punecon55413.2022.10014739" @default.
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