Application of Artificial Intelligence in predicting earthquakes: state-of-the-art and future challenges

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field

Attention-based bi-directional long-short term memory network for earthquake prediction

An earthquake is a tremor felt on the surface of the earth created by the movement of the major pieces of its outer shell. Till now, many attempts have been made to forecast earthquakes, which saw some success, but these attempted models are specific to a region. In this paper, an earthquake occurrence and location prediction model is proposed. After reviewing the literature, long short-term memory (LSTM) is found to be a good option for building the model because of its memory-keeping ability. Using the Keras tuner, the best model was selected from candidate models, which are composed of combinations of various LSTM architectures and dense layers. This selected model used seismic indicators from the earthquake catalog of Bangladesh as features to predict earthquakes of the following month. and Attention mechanism was added to the LSTM architecture to improve the model’s earthquake occurrence prediction accuracy, which was 74.67%. Additionally, a regression model was built using LSTM and dense layers to predict the earthquake epicenter as a distance from a predefined location, which provided a root mean square error of 1.25

Prevalence of and factors associated with childhood diarrhoeal disease and acute respiratory infection in Bangladesh: an analysis of a nationwide cross-sectional survey.

OBJECTIVES: This study aimed to estimate the prevalence of childhood diarrhoeal diseases (CDDs) and acute respiratory infections (ARIs) and also to determine the factors associated with these conditions at the population level in Bangladesh. SETTING: The study entailed an analysis of nationally representative cross-sectional secondary data from the most recent Bangladesh Demographic and Health Survey conducted in 2017-2018. PARTICIPANTS: A total of 7222 children aged below 5 years for CDDs and 7215 children aged below 5 years for ARIs during the survey from mothers aged between 15 and 49 years were the participants of this study. In the bivariate and multivariable analyses, we used Pearson χ2 test and binary logistic regression, respectively, for both outcomes. RESULTS: The overall prevalence of CDD and ARI among children aged below 5 years was found to be 4.91% and 3.03%, respectively. Younger children were more likely to develop both CDDs and ARIs compared with their older counterparts. Children belonging to households classified as poorest and with unimproved floor materials had a higher prevalence of diarrhoea than those from households identified as richest and with improved floor material, respectively. Stunted children had 40.8% higher odds of diarrhoea than normal children. Being male and having mothers aged below 20 years were 48.9% and two times more likely to develop ARI than female counterparts and children of mothers aged 20-34 years, respectively. Children whose mothers had no formal education or had primary and secondary education had higher odds of ARI compared with children of mothers having higher education. CONCLUSION: This study found that children aged below 24 months were at higher risk of having CDDs and ARIs. Thus, programmes targeting these groups should be designed and emphasis should be given to those from poorest wealth quintile to reduce CDDs and ARIs

Knowledge and awareness about food safety, foodborne diseases, and microbial hazards: A cross-sectional study among Bangladeshi consumers of street-vended foods

Maintaining quality and safety of street-vended foods (SVFs) is a challenge and a public health priority in low-and middle-income countries due to its affordability, availability and association with foodborne diseases and microbial hazards. The purpose of this study was to assess the knowledge and awareness of food safety, foodborne diseases, and microbial hazards among Bangladeshi consumers of SVFs. A cross-sectional survey was administered among 650 Bangladeshi adults who purchase and consume SVFs. The mean food safety knowledge score of consumers was 10.73 (SD = 2.84, range: 3–18), indicating moderate knowledge. Multiple linear regression analysis found that male consumers (B = -0.549, p < 0.030), consumers with ‘no formal education’ (B = −1.815, p < 0.045), and consumers with ‘secondary education’ (B = −1.476, p < 0.016) were less knowledgeable about food safety compared to their counterparts. Older consumers (36–45 years) were more knowledgeable about food safety compared to younger consumers (18–25 years) (B = 1.300, p < 0.011). Three-quarters of the respondents (76.9%) were not always confident about food safety issues when they bought SVFs, and affordability was the main reason (37.8%) for purchasing SVFs. Increased education and awareness on food safety education for Bangladeshi consumers of SVFs is needed, specifically targeting young adult males, and individuals with lower educational backgrounds