Earthquake Magnitude and Frequency Forecasting in Northeastern Algeria using Time Series Analysis

Merdasse, Mouna; Hamdache, Mohamed; Peláez, José A.; Henares, Jesús; Medkour, Tarek

doi:10.3390/app13031566

Cited by 6 publications

(3 citation statements)

References 98 publications

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“…Briefly, the estimation results â = −8.13277 and b = 21.37195. In addition, we also compare the model in Equation ( 19) with the autoregressive integrated moving average (ARIMA) (1, 1, 1) model, as follows [64]:…”

Section: Extreme Earthquake Frequency Processmentioning

confidence: 99%

Earthquake Bond Pricing Model Involving the Inconstant Event Intensity and Maximum Strength

Ibrahim,

Sukono,

Napitupulu

et al. 2024

Mathematics

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Traditional insurance’s earthquake contingency costs are insufficient for earthquake funding due to extreme differences from actual losses. The earthquake bond (EB) links insurance to capital market bonds, enabling higher and more sustainable earthquake funding, but challenges persist in pricing EBs. This paper presents zero-coupon and coupon-paying EB pricing models involving the inconstant event intensity and maximum strength of extreme earthquakes under the risk-neutral pricing measure. Focusing on extreme earthquakes simplifies the modeling and data processing time compared to considering infinite earthquake frequency occurring over a continuous time interval. The intensity is accommodated using the inhomogeneous Poisson process, while the maximum strength is modeled using extreme value theory (EVT). Furthermore, we conducted model experiments and variable sensitivity analyses on EB prices using earthquake data from Indonesia’s National Disaster Management Authority from 2008 to 2021. The sensitivity analysis results show that choosing inconstant intensity rather than a constant one implies significant EB price differences, and the maximum strength distribution based on EVT matches the data distribution. The presented model and its experiments can guide EB issuers in setting EB prices. Then, the variable sensitivities to EB prices can be used by investors to choose EB according to their risk tolerance.

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Section: Extreme Earthquake Frequency Processmentioning

confidence: 99%

Earthquake Bond Pricing Model Involving the Inconstant Event Intensity and Maximum Strength

Ibrahim,

Sukono,

Napitupulu

et al. 2024

Mathematics

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show abstract

“…For AFAD, it is reported that ML and M w scales are almost equivalent for a wide range of magnitude values (see [78] and references therein); thus, this catalog could be considered homogeneous with respect to magnitude (cf. a unified EQ catalog is necessary for this kind of analysis, see, e.g., [79]). There, beyond the cumulative FMD depicted by open squares, the noncumulative FMD [80] is also shown with open triangles.…”

Section: The Seismic Data Usedmentioning

confidence: 99%

Identifying the Occurrence Time of the Destructive Kahramanmaraş-Gazientep Earthquake of Magnitude M7.8 in Turkey on 6 February 2023

Sarlis,

Skordas,

Christopoulos

et al. 2024

Applied Sciences

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Here, we employ natural time analysis of seismicity together with non-extensive statistical mechanics aiming at shortening the occurrence time window of the Kahramanmaraş-Gazientep M7.8 earthquake. The results obtained are in the positive direction pointing to the fact that after 3 February 2023 at 11:05:58 UTC, a strong earthquake was imminent. Natural time analysis also reveals a minimum fluctuation of the order parameter of seismicity almost three and a half months before the M7.8 earthquake, pointing to the initiation of seismic electrical activity. Moreover, before this earthquake occurrence, the detrended fluctuation analysis of the earthquake magnitude time-series reveals random behavior. Finally, when applying earthquake nowcasting, we find average earthquake potential score values which are compatible with those previously observed before strong (M≥7.1) earthquakes. The results obtained may improve our understanding of the physics of crustal phenomena that lead to strong earthquakes.

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“…Its ability to analyze seasonality, periodicity, and trends in data make it highly versatile with accurate predictive effect. It has been utilized for morbidity prediction in medical research [29,30], commodity price and stock price forecasting in finance [31][32][33][34], as well as for applications in geology [35,36], transportation [37,38], electricity [39,40], sound recognition [41], atmospheric environment research [42], and other disciplines. In recent years, the rapid development of big data and artificial intelligence has led to long short-term memory (LSTM), a modified version of a recurrent neural network (RNN), becoming a prominent research topic.…”

Section: Regulated or Suggested Concentration Of Co 2 (Ppm)mentioning

confidence: 99%

Prediction and Comparison of In-Vehicle CO2 Concentration Based on ARIMA and LSTM Models

Han,

Lin,

Qin

2023

Applied Sciences

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An increase in the carbon dioxide (CO2) concentration within a vehicle can lead to a decrease in air quality, resulting in numerous adverse effects on the human body. Therefore, it is very important to know the in-vehicle CO2 concentration level and to accurately predict a concentration change. The purpose of this research is to investigate in-vehicle concentration levels of CO2, comparing the accuracy of an autoregressive integrated moving average (ARIMA) model and a long short-term memory (LSTM) model in predicting the change in CO2 concentration. We conducted a field test to obtain in-vehicle original concentration data of CO2 while driving, establishing a prediction model of CO2 concentration with ARIMA and LSTM. We selected mean absolute percentage error (MAPE) and root mean squared error (RMSE) as the evaluation indicators. The findings indicate the following: (1) With the vehicle windows closed and recirculation ventilation mode activated, in-vehicle CO2 concentration increases rapidly. During testing, CO2 accumulation rates were measured at 1.43 ppm/s for one occupant and 3.52 ppm/s for three occupants within a 20 min driving period. Average concentrations exceeded 1000 ppm, so it is recommended to improve ventilation promptly while driving. (2) The MAPE of ARIMA and LSTM prediction results are 0.46% and 0.56%, respectively. The RMSE results are 19.62 ppm and 22.76 ppm, respectively. The prediction results demonstrate that both models effectively forecast changes in a vehicle’s interior environment CO2, but the prediction accuracy of ARIMA is better than that of LSTM. The research findings provide theoretical guidance to traffic safety managers in selecting suitable models for predicting in-vehicle CO2 concentrations and establish an effective in-vehicle ventilation warning control system.

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Earthquake Magnitude and Frequency Forecasting in Northeastern Algeria using Time Series Analysis

Cited by 6 publications

References 98 publications

Earthquake Bond Pricing Model Involving the Inconstant Event Intensity and Maximum Strength

Earthquake Bond Pricing Model Involving the Inconstant Event Intensity and Maximum Strength

Identifying the Occurrence Time of the Destructive Kahramanmaraş-Gazientep Earthquake of Magnitude M7.8 in Turkey on 6 February 2023

Prediction and Comparison of In-Vehicle CO2 Concentration Based on ARIMA and LSTM Models

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