An integrated approach based earthquake risk assessment of a seismically active and rapidly urbanizing area in Northern Pakistan

Maqsoom, Ahsen; Aslam, Bilal; Khalil, Umer; Mehmood, Asad; Ashraf, Hassan; Siddique, Ali

doi:10.1080/10106049.2022.2105404

Cited by 9 publications

(3 citation statements)

References 83 publications

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“…The following are the most important studies related to the topic of research in comparing the most successful methods,the study by Maqsoom et al (2022) suggested two integrated frameworks: analytic network process (ANP)-artificial neural network (ANN) and ANP-convolutional neural network (CNN), and 16 factors contributing to earthquake risk were selected. Using geographic information system (GIS) to formulate it, a database was created for training and testing models, thus designing earthquake hazards in North Pakistan, and the area under the curve (AUC) values for ANN and CNN were 0.843 and 0.878, respectively, and this shows good performance [6]. The study aims for Tehseen et al (2020) to identify and compare the methods, models, frameworks and tools used to predict earthquakes using criteria based on 70 studies published in 2010-2020.…”

Section: Related Workmentioning

confidence: 99%

Earthquake prediction technique: a comparative study

Jarah¹,

Alasadi

Hashim

2023

IJ-AI

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Earthquakes are one of the most dangerous natural disasters facing humans because of their occurrence without warning and their impact on their lives and property. In addition, predicting seismic movement is one of the main research topics in seismic disaster prevention. In geological studies, scientists can predict and know the locations of earthquakes in the long term. Therefore, about 80% of the major global earthquakes lie along the Pacific Ring belt, known as the Ring of Fire. Machine learning methods have also been used for short-term earthquake prediction, and studies have applied the random forest method to determine the factors that precede earthquakes. The machine learning method was based on various decision trees, each of which predicted the time to the nearest oscillation. The third group of scientists used the hybrid prediction method, which combines machine learning and geological studies. This research deals with a review of most of the geological studies and machine learning techniques applied to earthquake data sets, which showed a total lack of prediction of potential earthquakes through one approach, so studies designed by geologists were combined with machine learning.

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Section: Related Workmentioning

confidence: 99%

Earthquake prediction technique: a comparative study

Jarah¹,

Alasadi

Hashim

2023

IJ-AI

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“…AI has huge potential to overcome such losses by developing intelligent systems for early prediction of such floods, risk assessment, and management [42]. Recently, the development of AI-based earthquake prediction systems has also improved the prediction and assessment of earthquakes in Pakistan [43].…”

Section: Pakistanmentioning

confidence: 99%

Middle East and North African Health Informatics Association (MENAHIA): Technological initiatives for ‘One Health’

Abd-alrazaq,

Al-Shorbaji,

Lakhdari

et al. 2023

Yearb Med Inform

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MENAHIA (Middle East and North African Health Informatics Association) is the International Medical Informatics Association chapter dedicated to the Middle East and North Africa region. This region is rapidly growing in terms of the use of health informatics or what has been recently coined “digital health”. Human health is highly affected by the health of the environment, animal health, food, nutrition, climate change, and many other factors that are beyond the biological or genetic structure of human beings. The impact of animal health and the health of the environment on people's health is an old phenomenon but recent reemerging and appearance of diseases have clearly demonstrated the link between these. The Novel Coronavirus disease (COVID-19) that almost all of us have been suffering from is an example of this.A number of countries in the region have already shown the depth and the work that they do to integrate the concept of ‘One Health’ in the public health surveillance system as they have described the work that has been done to capture data from databases other than those dealing with human beings. The examples that were provided to monitor the health of animals, agriculture, environmental health, climate change, and man-made and natural disasters are just examples of what countries have been registering in their databases and informing the health authorities of these changes and emerging trends.

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“…ANN studies demonstrated that these models undoubtedly outperform those traditional regression techniques in the sense of representation of the complicated links between seismic quantities and ground motions (Yaghmaei-Sabegh et al, 2012). Reportedly, Maqsoom et al, (2022) have undertaken a thorough assessment of the prevalent PGA prediction models and the call of a region-specific methodology taking root in the subsurface geology and the tectonics of the study area has been made evident (Cho et al, 2022;Gerstenberger et al, 2020;Ahmed et al, 2008). Till today, numerous pieces of research support the notion of incorporating modern machine learning techniques like ANNs in seismic prediction for increased disaster preparedness in earthquake prone areas; for example, Pakistan.…”

Section: Introductionmentioning

confidence: 99%

Ground motion prediction using Artificial Neural Network in Pakistan

MAHNOOR BIBI

2024

World J. Adv. Eng. Technol. Sci.

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The goal of this research project is to design, build, and validate an artificial neural network (ANN) model that predicts ground motion from the previous data of earthquake for seismic incidents in Pakistan. The prediction of ground motion is essential for determining the seismic risks and consequently providing measures to mitigate them. The ANN model implements activation function in hidden neurons to represent those relationships the seismic data holds and get logarithmic PGA values. The model performance evaluation metrics which are like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Correlation Coefficients prove the accuracy and robustness of the ANNPGA model. The ANNPGA model displays the best predictive power of all PGA values through the validation dataset. The MSE value is 0.00264 which model's accuracy in capturing ground motion variability. A comparative analysis with already created empirical and physics-based models will demonstrate that the ANPGA model gives more accurate predictions in most cases and especially in situations where nonlinear relationships are involved.

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An integrated approach based earthquake risk assessment of a seismically active and rapidly urbanizing area in Northern Pakistan

Cited by 9 publications

References 83 publications

Earthquake prediction technique: a comparative study

Earthquake prediction technique: a comparative study

Middle East and North African Health Informatics Association (MENAHIA): Technological initiatives for ‘One Health’

Ground motion prediction using Artificial Neural Network in Pakistan

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