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All Journal Scientechno: Journal of Science and Technology Journal of World Future Medicine, Health and Nursing
Munkhzul Ganbat
Mongolian State University of Education

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MODELING THE IMPACT OF SEA-LEVEL RISE ON COASTAL VULNERABILITY IN JAKARTA USING AN INTEGRATED DATA SCIENCE FRAMEWORK Nofirman Nofirman; Dulguun Amarsaikhan; Munkhzul Ganbat; Tugsuu Jargalsaikhan
Scientechno: Journal of Science and Technology Vol. 4 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i3.2669

Abstract

Jakarta, the capital city of Indonesia, is highly vulnerable to the impacts of sea-level rise due to its coastal location, rapid urbanization, and subsidence, making it crucial to understand how climate change-driven increases in sea levels affect the city’s coastal areas for effective adaptation planning. This study aims to model the impact of sea-level rise on the vulnerability of Jakarta’s coastal zones by using an integrated data science framework to assess potential risks such as flooding, land loss, and other environmental consequences under various sea-level rise scenarios. Employing a combination of geographic information systems (GIS), remote sensing data, and machine learning models, the analysis integrates sea-level rise projections with land elevation, population density, and infrastructure data to evaluate potential impacts, while algorithms such as Random Forest and Support Vector Machine (SVM) are utilized to predict vulnerability levels. The results indicate that Jakarta’s coastal areas face high vulnerability, with substantial portions of land projected to be inundated under higher sea-level scenarios, particularly in low-lying and densely populated regions at heightened risk of flooding and infrastructure damage. Overall, this research offers valuable insights into future coastal vulnerability in Jakarta and demonstrates how an integrated data science approach can support urban planning and climate adaptation strategies aimed at reducing the risks associated with rising sea levels.
Big Data and Epidemiology: Predictive Models for Future Infectious Disease Outbreaks Munkhzul Ganbat; Baatar Tserendorj; Selenge Batbold; Rustiyana Rustiyana
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i2.2805

Abstract

Intensifying global mobility, climate variability, and urban density have increased the frequency and complexity of infectious disease outbreaks, prompting the need for more accurate and timely epidemiological surveillance. Big Data analytics has emerged as a transformative approach capable of integrating heterogeneous datasets to detect patterns that traditional surveillance systems often miss. This study aims to examine the effectiveness of predictive modeling techniques leveraging Big Data sources such as social media activity, electronic health records, mobility data, and environmental indicators in forecasting potential infectious disease outbreaks. A mixed-methods analytical design was employed, combining machine learning based predictive modeling with retrospective epidemiological validation using multi-country datasets covering the past ten years. The results show that ensemble learning models, especially random forest and gradient boosting algorithms, significantly outperform conventional statistical models in predicting outbreak onset and trajectory, achieving higher accuracy, sensitivity, and early-warning lead time. The findings demonstrate that Big Data driven predictive models can enhance public health preparedness by providing earlier and more reliable outbreak alerts. The study concludes that integrating Big Data analytics into national and global epidemiological systems is essential for strengthening proactive disease prevention, although ethical governance and data privacy protections must be prioritized.
Co-Authors Baatar Tserendorj Dulguun Amarsaikhan Nofirman, Nofirman Rustiyana, Rustiyana Selenge Batbold Tugsuu Jargalsaikhan