Earthquakes magnitude prediction using deep learning for the Horn of Africa

Abebe, Ewnetu; Kebede, Hailemichael; Kevin, Mickus; Demissie, Zelalem

doi:10.1016/j.soildyn.2023.107913

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…This research aims to implement methods of earthquake prediction that have already shown success in the Horn of Africa. The Horn of Africa experiment was able to predict earthquakes down to a 28.87% error using machine learning algorithms (Abebe et al, 2023). This is a startlingly low margin of error, for the field of earthquake prediction.…”

Section: Problem Statementmentioning

confidence: 91%

“…As each model was run the graph of its training loss and validation loss were recorded. The training loss indicates how well the model is fitting the training data, while the validation loss indicates how well the model fits new data (Abebe et al, 2023). When the training loss is greater than the validation loss the model is overfitting data, meaning that it is picking up too much noise to make accurate predictions.…”

Section: Model Performancementioning

confidence: 99%

“…Machine learning allows for many leaps forward in data processing that were previously impossible, especially in terms of the number of data points and variables that can be processed. This all culminated in the Horn of Africa experiment where four of the most recently developed machine learning algorithms are tested against each other for a comparison of accuracy in the Horn of Africa (Abebe et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Tectonic Setting on Machine Learning Approaches for Earthquake Prediction

Taskinen,

Demissie

2023

Preprint

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In prior decades the concept of using mathematical methods to predict earthquakes was considered infeasible. Recent advances in machine learning and predictive modeling offer promising avenues to potentially realize earthquake prediction. In order to test the viability of machine learning methods, experiments were made with earthquake datasets from Kansas and Puerto Rico. The two datasets were chosen for the distinct differences in their tectonic settings. Kansas has few major faults, with a largely inactive subsurface, this produced a smaller dataset with a few large clusters. Puerto Rico is complexly faulted, with an extremely active tectonic setting, this produced a larger dataset with a large number of small clusters. In order to test the effectiveness of these two datasets for machine learning and prediction they were run through three different machine learning algorithms including an LSTM model, Bi-LSTM model, Bi-LSTM model with attention. Not only were the three different machine learning methods compared against each other for accuracy but also the datasets as well. Conclusive findings show that the two different data sets favor different processing methods. The Kansas data performs the best with the Bi-LSTM with attention model, while the Puerto Rico data performs the best with the LSTM model. This is likely due to the tectonic settings of the two regions, since the Kansas dataset has less overall data, and earthquakes are concentrated in a few large clusters, while the Puerto Rico data set has a more even distribution.

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Section: Problem Statementmentioning

confidence: 91%

Section: Model Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Impact of Tectonic Setting on Machine Learning Approaches for Earthquake Prediction

Taskinen,

Demissie

2023

Preprint

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“…, 2023), climate change (Nwokolo et al. , 2023), earthquakes (Abebe et al ., 2023) and urban floods (Motta et al. , 2021).…”

Section: Introductionmentioning

confidence: 99%

“…With the advent of artificial intelligence (AI), recent studies have begun to explore machine and deep learning algorithms to detect disasters as well as to identify risk factors, such as for forest fires (Saha et al, 2023), climate change (Nwokolo et al, 2023), earthquakes (Abebe et al, 2023) and urban floods (Motta et al, 2021). Both machine learning and deep learning come with huge advantages for its implementation in wide variety of matters.…”

mentioning

confidence: 99%

Fatal structure fire classification from building fire data using machine learning

Balakrishnan,

Mohammed Hashim,

Lee

et al. 2023

IJICC

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PurposeThis study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.Design/methodology/approachExploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.FindingsThe main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).Research limitations/practical implicationsLimitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.Originality/valueThe study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.

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Exploiting the synergy of SARIMA and XGBoost for spatiotemporal earthquake time series forecasting

Kaushal,

Gupta,

Sehgal

2024

Earth Surf Processes Landf

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Earthquakes are vibrations that occur on the surface of earth, generating fires, ground shaking, tsunamis, landslides and cracks. These incidents can cause severe damage and loss of life. Accurate earthquake forecasts are critical for anticipating and mitigating these hazards, which can avoid damage to buildings and infrastructure and save lives. To address the challenges given by earthquakes probabilistic nature, this paper presents a hybrid SARIMA–XGBoost approach to earthquake magnitude prediction. The suggested technique consists of a two‐step process: an exploration phase that uses exploratory data analysis, which includes descriptive statistics and data visualisation, and a prediction phase that focusses on forecasting future earthquakes. Using a large significant earthquake dataset spanning 1965–2023, the study intends to gain insights and lessons for more effective earthquake prediction methods. Further, in a comparison analysis, the results of SARIMA‐XGBoost model are compared to those of traditional ARIMA and SARIMA models. The results highlight the superior performance of the hybrid SARIMA–XGBoost model, showcasing a mean absolute error (MAE) of 0.038, a mean squared error (MSE) of 0.0040, and a root mean squared error (RMSE) of 0.068. These metrics collectively underscore the model's enhanced accuracy in forecasting earthquake magnitudes. The notably low values of MAE, MSE and RMSE indicate that our hybrid approach significantly improves prediction accuracy compared to alternative models. By integrating SARIMA's time series (TS) analysis with XGBoost's machine learning (ML) capabilities, the hybrid model reduces forecasting errors more effectively, demonstrating its clear advantage in precision.

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Earthquakes magnitude prediction using deep learning for the Horn of Africa

Cited by 13 publications

References 8 publications

The Impact of Tectonic Setting on Machine Learning Approaches for Earthquake Prediction

The Impact of Tectonic Setting on Machine Learning Approaches for Earthquake Prediction

Fatal structure fire classification from building fire data using machine learning

Exploiting the synergy of SARIMA and XGBoost for spatiotemporal earthquake time series forecasting

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