Earthquake threats can result in fatalities, property destruction, and other cascading effects. Since it is nearly impossible to prevent earthquakes, anticipating the location of future earthquakes and figuring out their likelihood could be very helpful in reducing the seismic threat. In this work, seismic hazard prediction is executed to forecast adverse results using a range of potential artificial intelligence (AI) techniques, including ML and ANN. In the case study, we have looked at Turkey, which was recently and badly damaged by two earthquakes in February 2023. To predict earthquake magnitude, this study used a variety of regression algorithms, including Decision Tree Regressor, Extra-Trees Regressor, Random
Forest Regressor, Bayesian Ridge Regressor, and advanced gradient boosting decision tree (GBDT) algorithms such as XGBoost, LightGBM, and CatBoost, as well as three artificial neural networks (ANN). The predicted magnitude and risk zone of an earthquake are mapped using a geographic information system (GIS), and the maps performed well in terms of prediction. The generated maps is showing the expected earthquake risk based on historical data using the statistical computations. The ANN models perform exceptionally well, with R2 scores of 0.99 and 0.98 for training and case study data, respectively, and low values for MSE, MAE, and RMSE. ML models have demonstrated an exceptional ability to properly generalize from a single dataset, which implies they can accurately anticipates results for new and untested data.
The results would be helpful to many local emergency preparedness and infrastructure planning organizations.
The following research work in the village named "Ghatampur" in Bhadohi of Uttar Pradesh state was to study the after-effects of the COVID-19 on the social behavioural changes among the villagers. The Covid-19 pandemic drastically changed the lives of the villagers with a few months of impact in India. The subsequent after-effects of the pandemic broke the normal life cycle and halted the normal village life. Many people from the village who worked in the Tier-1 cities of the country in the west like Mumbai lost their jobs; they were facing absolute monetary problems, no food, insecurities, no upcoming future to start again in the city for the near distant future, and last but not the least the threat to death due to Covid-19. These factors affected so profoundly the villagers from inside that a sort of crisis broke down among them, and migrant workers started returning to their respective villages. Ghatampur is one of these Covid-19 affected villages where migrant workers returned to their village with many difficulties, and certainly, it was not so easy. It surely affected the existing relationship between the villagers, among the family members, even in some cases there was extra stress, distress, agony due to the sudden job loss, insecurity related to the near future, the burden of carrying forward the huge family load with a bright future. The existing relationships between the villagers were stained to some extent, and the existing social factors do affect, to some extent, the behavioural pattern of the villagers. With a request from FARF (Fundamental Action and Research Foundation), we have chosen this research topic to deeply study the after-effects of the pandemic in social behaviour of the villagers. FARF organization has been doing an excellent job of social works like educating girls from lower castes of nearby villages, helping the distressed villagers provide food during prolonged lockdown, raising awareness among the villagers, etc. They wanted to study the after-effects of the pandemic on the village to get a general idea about how this
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