Ashraful Islam
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Ashraful Islam has a total of 44 co-authors. Here are the top co-authors by number of shared publications:
- Sanzida Murshed
Department of Geology, University of Dhaka, Dhaka 1000, Bangladesh
6 shared publications - Amy L. Griffin
RMIT University
5 shared publications David J. Paull
4 shared publicationsSanzida Murshed
4 shared publicationsMonira Jahan Tania
3 shared publicationsArifur Rahman Rifath
2 shared publications
Latest Publications
- 24 September 2026
- Published by Springer Nature in Journal of the Indian Society of Remote Sensing
- Vol. 54 (13) , 5067-5079
An Integrated Geospatial Approach to Unveiling shoreline Resilience Along the Barguna–Patuakhali Coast, Bangladesh
- 24 September 2026
- Published by Springer Nature in Journal of the Indian Society of Remote Sensing
- Vol. 54 (13) , 5067-5079
An Integrated Geospatial Approach to Unveiling shoreline Resilience Along the Barguna–Patuakhali Coast, Bangladesh
- Open Access
- 1 June 2026
- Published by KEAI Publishing in Natural Hazards Research
- Vol. 6 (2) , 430-448
A holistic geospatial framework for assessing disaster resilience in coastal Bangladesh
- 1 June 2026
- Published by KEAI Publishing in Natural Hazards Research
- Vol. 6 (2) , 430-448
Open AccessA holistic geospatial framework for assessing disaster resilience in coastal Bangladesh
- 24 March 2026
- Published by Springer Nature in Earth Systems and Environment
- Vol. 10 (8) , 9657-9681
Predicting Climate-Driven Soil Erosion in Bangladesh Using Fuzzy-AHP and Ensemble Machine Learning
- 24 March 2026
- Published by Springer Nature in Earth Systems and Environment
- Vol. 10 (8) , 9657-9681
Predicting Climate-Driven Soil Erosion in Bangladesh Using Fuzzy-AHP and Ensemble Machine Learning
- 4 March 2026
- Published by Springer Nature in Journal of Coastal Conservation
- Vol. 30 (2) , 15
Assessing habitat quality in the sundarbans mangrove ecosystem of Bangladesh using the invest model
- 4 March 2026
- Published by Springer Nature in Journal of Coastal Conservation
- Vol. 30 (2) , 15
Assessing habitat quality in the sundarbans mangrove ecosystem of Bangladesh using the invest model
- Open Access
- 1 March 2026
- Published by KEAI Publishing in Natural Hazards Research
- Vol. 6 (1) , 122-140
Flood vulnerability assessment in Jamuna river floodplain: A Synthesis of multi-dimensional vulnerability indicators using geospatial and Fuzzy-AHP techniques
- 1 March 2026
- Published by KEAI Publishing in Natural Hazards Research
- Vol. 6 (1) , 122-140
Open AccessFlood vulnerability assessment in Jamuna river floodplain: A Synthesis of multi-dimensional vulnerability indicators using geospatial and Fuzzy-AHP techniques
- 8 December 2025
- Published by Springer Nature in Earth Systems and Environment
- Vol. 10 (5) , 6429-6450
An Ensemble Machine Learning Framework for Predicting Agricultural Droughts in the Ganges Delta of Bangladesh
- 8 December 2025
- Published by Springer Nature in Earth Systems and Environment
- Vol. 10 (5) , 6429-6450
An Ensemble Machine Learning Framework for Predicting Agricultural Droughts in the Ganges Delta of Bangladesh
- Open Access
- 18 January 2025
- Published by Elsevier in Geosystems and Geoenvironment
- Vol. 4 (2)
Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections
- 18 January 2025
- Published by Elsevier in Geosystems and Geoenvironment
- Vol. 4 (2)
Open AccessPredicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections
- Open Access
Landslide Studies in the Context of Disaster Management in Bangladesh—A Systematic Literature Review
Landslides and their resulting impacts on property and human life have become an ongoing challenge in the hilly regions of Bangladesh. This study aims to systematically review diverse landslide studies in Bangladesh, particularly focusing on landslide disaster management (LDM) from 2008 to 2023, encompassing the pre-disaster, syn-disaster, and post-disaster phases. Several key attributes of landslide studies were considered, including general trends, data types, study scales, contributing factors, methodologies, results, and validation approaches, to investigate challenges and subsequently identify research gaps. This study evaluated 51 research articles on LDM using a systematic literature review (SLR) technique that adhered to the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) framework. Our finding revealed that articles on LDM were dominated by the pre-disaster (76%) and the syn-disaster phases (12%), with the post-disaster phase (12%) receiving equal attention. The SLR revealed a growing number of studies since 2020 that used data-driven methods and secondary spatial data, often focused on medium-scale analyses (district level) that, however, often lacked field-based validation. From the factors examined in various landslide studies, topographical and hydrological factors were found to be the most significant attributes in assessment. This study identified key challenges, such as insufficient landslide inventories including poor site accessibility and a lack of high-resolution geological, soil, and rainfall data. It also highlighted critical research gaps, including the need for advanced technologies in susceptibility mapping for national hazard atlas, the investigation of underexplored causative factors, effective early warning systems, detailed post-event characterization, health impact assessment, risk-sensitive land use planning, and interactive web portals for landslide prone areas. This study would thus aid researchers in understanding the depth of existing knowledge and provide insights into how landslides fit into broader disaster management frameworks, facilitating interdisciplinary approaches.
Open AccessLandslide Studies in the Context of Disaster Management in Bangladesh—A Systematic Literature Review
Landslides and their resulting impacts on property and human life have become an ongoing challenge in the hilly regions of Bangladesh. This study aims to systematically review diverse landslide studies in Bangladesh, particularly focusing on landslide disaster management (LDM) from 2008 to 2023, encompassing the pre-disaster, syn-disaster, and post-disaster phases. Several key attributes of landslide studies were considered, including general trends, data types, study scales, contributing factors, methodologies, results, and validation approaches, to investigate challenges and subsequently identify research gaps. This study evaluated 51 research articles on LDM using a systematic literature review (SLR) technique that adhered to the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) framework. Our finding revealed that articles on LDM were dominated by the pre-disaster (76%) and the syn-disaster phases (12%), with the post-disaster phase (12%) receiving equal attention. The SLR revealed a growing number of studies since 2020 that used data-driven methods and secondary spatial data, often focused on medium-scale analyses (district level) that, however, often lacked field-based validation. From the factors examined in various landslide studies, topographical and hydrological factors were found to be the most significant attributes in assessment. This study identified key challenges, such as insufficient landslide inventories including poor site accessibility and a lack of high-resolution geological, soil, and rainfall data. It also highlighted critical research gaps, including the need for advanced technologies in susceptibility mapping for national hazard atlas, the investigation of underexplored causative factors, effective early warning systems, detailed post-event characterization, health impact assessment, risk-sensitive land use planning, and interactive web portals for landslide prone areas. This study would thus aid researchers in understanding the depth of existing knowledge and provide insights into how landslides fit into broader disaster management frameworks, facilitating interdisciplinary approaches.
- Open Access
- 10 October 2024
- Published by Elsevier in Environmental Challenges
- Vol. 17
Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios
- 10 October 2024
- Published by Elsevier in Environmental Challenges
- Vol. 17
Open AccessFlash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios