Assessing the Influence of Land Cover and Climate Change Impacts on Runoff Patterns Using CA-ANN Model and CMIP6 Data

Datos Bibliográficos

ID22033808
AutoresMahfuzur Rahman (0000-0002-3386-9613, Kunsan National University, autor de correspondencia), Mahfuzur Rahaman (0000-0001-8402-156X, Kunsan National University, autor de correspondencia), Md Monirul Islam (0000-0001-5875-433X, International University of Business Agriculture and Technology), Hyeong-Joo Kim (0000-0003-1365-6992, Kunsan National University), Shamsher Sadiq (0000-0002-9117-9856, Kunsan National University), Mehtab Alam (0000-0002-5866-1691, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology), Taslima Siddiqua (International University of Business Agriculture and Technology), Md Al Mamun (0000-0002-6540-9195, International University of Business Agriculture and Technology), Md Ashiq Hossen Gazi (International University of Business Agriculture and Technology), Matiur Rahman Raju (International University of Business Agriculture and Technology), Ningsheng Chen (0000-0002-6135-0739, Chinese Academy of Sciences), Md Alamgir Hossain (0000-0002-7368-6424, People's University of Bangladesh), Ashraf Dewan (0000-0001-5594-5464, Curtin University)
Año2023
Volumen12
Número10
Páginas401
Fecha de publicación2023-10-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaISPRS International Journal of Geo-Information (JOURNAL)
Identificadores de la revistaISSN: 2220-9964 • E-ISSN: 2220-9964
EditorialMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi12100401
OpenAlexW4387264859
IdiomaEN
Referencias citadas71

Dhaka city is experiencing rapid land cover changes, and the effects of climate change are highly visible. Investigating their combined influence on runoff patterns is vital for sustainable urban planning and water resources management. In this work, multi-date land cover classification was performed using a random forest (RF) algorithm. To validate accuracy of land cover classification, an assessment was conducted by employing kappa coefficient, which ranged from 85 to 96%, indicating a high agreement between classified images and the reference dataset. Future land cover changes were forecasted with cellular automata-artificial neural network (CA-ANN) model. Further, soil conservation service -curve number (SCS-CN) rainfall-runoff model combined with CMIP6 climate data was employed to assess how changes in land cover impact runoff within Dhaka metropolitan development plan (DMDP) area. Over the study period (2020–2100), substantial transformations of land cover were observed, i.e., built-up areas expanded to 1146.47 km2 at the end of 2100, while agricultural areas and bare land diminished considerably. Consequently, monsoon runoff increased from 350.14 to 368.24 mm, indicating elevated hydrological responses. These findings emphasized an intricate interplay between urban dynamics and climatic shifts in shaping runoff patterns, underscoring urgency of incorporating these factors into urban planning strategies for sustainable water resources management in a rapidly growing city such as Dhaka

Climate change · Land cover · Land use · Runoff curve number · Surface runoff · Environmental Science · Flood Risk Assessment and Management · Hydrology and Watershed Management Studies · Land Use and Ecosystem Services · Ecology