Earthquake Magnitude and Grid-Based Location Prediction using Backpropagation Neural Network

Priambodo, Bagus; Mahmudy, Wayan Firdaus; Rahman, Muhammad Arif

doi:10.17977/um018v3i12020p28-39

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…Neural Network (NN) is a model of information inspired by the biological nervous system. NN have been successfully implemented in several studies for prediction and classification [1], [7], [8]. NN gets knowledge through several learning processes.…”

Section: Neural Networkmentioning

confidence: 99%

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Hamdianah

Mahmudy

Widaryanto

2021

JITeCS

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Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required. The predictions are carried out to find out the annual yield of rice in an area. Weather factors greatly affect production results so that in this study using weather parameters as input parameters. The Input Parameters are used in the Recurrent Neural Network algorithm with the Backpropagation learning process. The results are compared with Neural Networks with Backpropagation learning to find out the most effective method. In this study, the Recurrent Neural Network has better prediction results compared to a Neural Network. Based on the computational experiments, it is found that the Recurrent Neural Network obtained a Means Square Error of 0.000878 and a Mean Absolute Percentage Error of 10,8832%, while the Neural Network obtained a Means Square Error of 0.00104 and a Mean Absolute Percentage Error of 10,3804.

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Section: Neural Networkmentioning

confidence: 99%

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Hamdianah

Mahmudy

Widaryanto

2021

JITeCS

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“…Prediction of open unemployment rates can be made by various methods, such as Simple Linear Regression [5], Regression Tree [11], ANFIS [12], and Backpropagation [6]. While in this study uses the Backpropagation method because it has been proven to work well in various problems, such as regression, pattern recognition, and prediction [13]. As for the predictions that have been done using the Backpropagation method, namely [14] With, the best architectural network model is 18-5-1, obtained MSE of 0.000998685.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Tuban Regency Open Unemployment Rate in 2022 with the Backpropagation Method

Putri

Hamid

Novitasari

2022

Numerical: J.Mat and Pend.Mat

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The Open Unemployment Rate is the number of unemployed from the total labor force in an area or region in the form of a percentage. In Tuban Regency, unemployment has experienced a very significant increase from 2019 to 2020. Therefore, it is necessary to make predictions in 2021 and 2022 to find out the description of the Open Unemployment Rate in that year so that actions can be taken by the government to deal with this. By using the Backpropagation method, this study obtained predictions that in 2021 it will be 4.58% of the total workforce in Tuban Regency in 2021 and in 2022 the open unemployment rate will be 4.7% of the total workforce in Tuban Regency in 2022, with network architecture 2-70-1-1, the learning rate used is 0.1 and the MAPE value is 8.363%. The test was stopped at the 276th epoch. At the time of testing between the output and the target there was a correlation (R) of 0.993 where the best result was 1.

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“…They include the study of mathematical models for earthquake prediction, machine learning methods and testing earthquake prediction algorithms [20][21][22][23][24][25][26][27][28][29]. Many articles are devoted to earthquake prediction using artificial neural networks [30,31] and more sophisticated methods, including hybrid neural network [32], and recurrent neural network [33], and a lot of other machine learning methods [34]. Despite the relatively positive results claimed in those studies, some simpler models can offer similar or better predictive powers [35].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Performance of GPS Data for Earthquake Prediction

2021

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The results of earthquake prediction largely depend on the quality of data and the methods of their joint processing. At present, for a number of regions, it is possible, in addition to data from earthquake catalogs, to use space geodesy data obtained with the help of GPS. The purpose of our study is to evaluate the efficiency of using the time series of displacements of the Earth’s surface according to GPS data for the systematic prediction of earthquakes. The criterion of efficiency is the probability of successful prediction of an earthquake with a limited size of the alarm zone. We use a machine learning method, namely the method of the minimum area of alarm, to predict earthquakes with a magnitude greater than 6.0 and a hypocenter depth of up to 60 km, which occurred from 2016 to 2020 in Japan, and earthquakes with a magnitude greater than 5.5. and a hypocenter depth of up to 60 km, which happened from 2013 to 2020 in California. For each region, we compare the following results: random forecast of earthquakes, forecast obtained with the field of spatial density of earthquake epicenters, forecast obtained with spatio-temporal fields based on GPS data, based on seismological data, and based on combined GPS data and seismological data. The results confirm the effectiveness of using GPS data for the systematic prediction of earthquakes.

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Earthquake Magnitude and Grid-Based Location Prediction using Backpropagation Neural Network

Cited by 11 publications

References 19 publications

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Prediction of the Tuban Regency Open Unemployment Rate in 2022 with the Backpropagation Method

Analyzing the Performance of GPS Data for Earthquake Prediction

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