SELS https://ar.iub.edu.bd/handle/11348/13 2026-09-22T06:16:28Z 2026-09-22T06:16:28Z Shuvo, Ragib Mahmood https://ar.iub.edu.bd/handle/11348/1566 2026-09-06T07:35:20Z 2026-09-01T00:00:00Z

Delineation of fresh groundwater zone in the coastal aquifers of Bangladesh: a multi-modal approach by remote sensing and machine learning algorithm attribution Shuvo, Ragib Mahmood Groundwater Salinity Intrusion in the coastal belts of Bangladesh poses a severe threat to the livability and livelihood of more than 20 million populations due to scarcity of freshwater. Climate change and anthropogenic activities is further exacerbating the salinity intrusion risks across these regions. This research aims to delineate the fresh groundwater potential zones in the six districts of the southwestern part of the coastal areas of Bangladesh with the help of remote sensing and machine learning approaches. 15 parameters were primarily selected to prepare the spatial layers which represent the topographic, meteorological, hydro-chemical and land use characteristics of the study area. Among these, 10 parameters were finally retained by multiple iterations of the Variable Inflation Factor (VIF) analysis at a threshold of 10 in order to exclude multicollinearity of the parameters. 117 groundwater samples for wet and dry season were collected from Bangladesh Water Development Board (BWDB) to classify the training and testing dataset. Synthetic Minority Oversampling Technique (SMOTE) was applied to the training dataset in order to address the imbalance of fresh and saline groundwater sample size. Five Machine Learning algorithms namely, Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (xgboost), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) were applied to delineate the fresh groundwater potential zones. The accuracy of the ML models was validated by using ROC/AUC, Precision-Recall, Sensitivity, Specificity, F-1 Score, Kappa accuracy. RF and xgboost delineated the potential zones with the most accuracy, having an overall accuracy of 95.83%, AUC of 0.99, an average precision of 0.917, and zero false negatives on the testing dataset. The potential zones were divided into five classes and approximately 8-10% of the study area were delineated as very high potential zone for fresh groundwater with majority of the study area falling under very low potential areas. The inability to delineate similar outputs by other ML models highlighted the limitations in collecting, managing and storing spatially distributed groundwater quality data across the region and the severe heterogeneity of fresh and non-fresh groundwater quality within the sample dataset. The findings provide an emphasis on combining hydro-chemical data in groundwater potential zone mapping, especially in the coastal areas where further increase in climate change is going to shift the dynamics of freshwater resources. Therefore, similar research can drive anticipatory actions to conserve groundwater quality and establish a sustainable groundwater governance in similar vulnerable areas. This thesis is submitted in partial fulfilment of the requirements for the degree of Master of Science (M. Sc.) in Climate Change And Development 2026.

2026-09-01T00:00:00Z Chakrabarty, Rhiney https://ar.iub.edu.bd/handle/11348/1082 2026-07-08T14:35:15Z 2025-12-01T00:00:00Z

Internship at Mymensingh Medical College Hospital Chakrabarty, Rhiney This internship was undertaken as a partial requirement for the Bachelor of Science in Microbiology and was completed at Mymensingh Medical College Hospital, where I gained practical exposure to real-world clinical and laboratory environments. The primary objective of this internship was to bridge theoretical knowledge with hands-on experience by observing and participating in routine diagnostic procedures, laboratory operations, sample handling, and data interpretation under professional supervision. Throughout the internship, I had the opportunity to work closely with experienced medical technologists and faculty members, enabling me to understand the workflow of clinical microbiology, patient sample processing, infection detection methods, and laboratory safety practices. This experience not only broadened my academic learning but also contributed to the development of essential professional skills such as communication, discipline, teamwork, and ethical responsibilities in patient-centered environments. Overall, the internship at Mymensingh Medical College Hospital served as a valuable foundation for my future career in microbiology and the broader health sector.

2025-12-01T00:00:00Z Chakrabarty, Rhiney https://ar.iub.edu.bd/handle/11348/1081 2026-07-08T14:35:14Z 2025-12-01T00:00:00Z

Biofilm Mediated Complications in Hospital and Environmental Setting Chakrabarty, Rhiney Biofilms are complex microbial communities that attach firmly to surfaces and grow within a dense extra cellular matrix composed of polysaccharides, proteins, and nucleic acids. Their ability to anchor to medical devices, hospital surfaces, industrial pipelines, and food processing equipment makes them a major concern for both healthcare and industry. In hospital environments, biofilms act as persistent reservoirs of pathogenic microorganisms. They reduce the activity of antibiotics, protect harmful bacteria from the host immune system, and play a central role in long-lasting infections linked with catheters, prosthetic implants, ventilator tubes, and surgical instruments. These microbial structures also contaminate critical areas such as operating theatres, sinks, and water outlets, which increases the risk of hospital-acquired infections and elevates patient morbidity and mortality.

2025-12-01T00:00:00Z Iqbal, Iqbal https://ar.iub.edu.bd/handle/11348/1074 2026-07-08T14:19:17Z 2026-01-01T00:00:00Z

Impact of Urbanization on Urban Climate: A GIS and Remote Sensing Approach on Dhaka City (2000-2025) Iqbal, Iqbal Unplanned and swift urbanization from 2000-2025 has changed Dhaka’s land use and climate significantly. GIS and remote sensing were utilized in this thesis to quantify land cover change and the impact of the urban heat island (UHI) effect in Dhaka City. Four land cover categories (Built-up, Vegetation, Open Land, Water,) were classified through using multi-date Landsat satellite images (2000, 2010, 2020, 2025) to assess spatiotemporal transformations. To retrieve land surface temperatures (LST) for each period, thermal infrared data were processed which enabled analysis of how urban expansion affects and increases heating of the surface. Methodology included change detection, supervised classification, and LST retrieval, validated with field data and literature. Findings of a sharp increase in built-up area (from 29% to 41% of the area) and loss of vegetated land (from 28% to 11%) between 2000 and 2025 were recorded. Higher mean and maximum LST in urbanized zones were correlated with these land cover shifts, emphasizing the increasing UHI effect. Hottest surfaces were discovered in developed bare lands recently and also in dense urban areas. Water bodies and green spaces remained cooler on average. Some irregularity in LST patterns were observed due to seasonal differences in image acquisition, highlighting the need for interpretation cautiously. Dhaka’s urban expansion has led to greater heat retention and spatially increased UHI hotspots according to the findings of this research. Academic contributions of this thesis was by exploring updated, spatially explicit evidence of relation between land cover change and urban climate in Dhaka. These insights are significant for urban environmental management, illustrating the critical importance of sustainable planning interventions (e.g. preserving green infrastructure) to mitigate rising urban heat and improve livability in megacities like Dhaka.

2026-01-01T00:00:00Z