Implementing multi-criteria decision-making approaches to evaluate and map drought vulnerability with Geospatial Artificial Intelligence (Geo-AI) based investigations
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| Publicado en: | SN Applied Sciences vol. 7, no. 10 (Oct 2025), p. 1185 |
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| Outros autores: | , , , |
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Springer Nature B.V.
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| Acceso en liña: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1007/s42452-025-07189-6 |2 doi | |
| 035 | |a 3260580306 | ||
| 045 | 2 | |b d20251001 |b d20251031 | |
| 100 | 1 | |a Shalwee |u Amity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh, Noida, India (GRID:grid.444644.2) (ISNI:0000 0004 1805 0217) | |
| 245 | 1 | |a Implementing multi-criteria decision-making approaches to evaluate and map drought vulnerability with Geospatial Artificial Intelligence (Geo-AI) based investigations | |
| 260 | |b Springer Nature B.V. |c Oct 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Assessing and mitigating drought vulnerability is crucial in the context of global climate change. This research aims to improve the precision of drought vulnerability assessments in future. The study identifies key parameters that influence drought vulnerability and integrates environmental, social, and economic factors to develop a comprehensive framework for accurate drought vulnerability mapping. This enhanced accuracy is vital for resource management and mitigation planning in drought-prone regions. The study focuses on the Ranchi district in Jharkhand, India, to analyse various types of droughts with suitable drought vulnerability parameters. An expert survey was conducted to consider the significance of these parameters that implement multi-criteria decision-making (MCDM) entropy approaches. The study weighted parameters according to different drought types, including meteorological, hydrological, and agricultural, for detailed analysis. The findings highlight the potential of MCDM methods in generating high-resolution and accurate drought vulnerability assessments, significantly contributing to sustainable water resource management and resilience-building efforts. The synthesis of spatial data from remote sensing and GIS with socio-economic, environmental, and hydrological criteria creates a comprehensive drought vulnerability model. The final drought vulnerability map for the district revealed that 15% of the area is under severe drought conditions, while 71% is moderately affected. Hydrological drought was identified as the primary cause of vulnerability, indicating a critical need to improve the district's hydrological reservoirs. Geo-AI techniques, such as machine learning and deep learning algorithms, will analyse complex spatial and temporal patterns in drought-affected regions. This research highlights the transformative role of AI-driven decision-making frameworks in addressing complex environmental challenges to provide valuable insights for policymakers and resource managers in implementing targeted drought mitigation strategies. | |
| 651 | 4 | |a India | |
| 653 | |a Climate change | ||
| 653 | |a Spatial analysis | ||
| 653 | |a Socioeconomic factors | ||
| 653 | |a Humidity | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Water resources management | ||
| 653 | |a Assessments | ||
| 653 | |a Surface water | ||
| 653 | |a Trends | ||
| 653 | |a Multiple criterion | ||
| 653 | |a Drought | ||
| 653 | |a Remote sensing | ||
| 653 | |a Population density | ||
| 653 | |a Hydrology | ||
| 653 | |a Machine learning | ||
| 653 | |a Decision making | ||
| 653 | |a Economic factors | ||
| 653 | |a Deep learning | ||
| 653 | |a Radiation | ||
| 653 | |a Resource management | ||
| 653 | |a Economics | ||
| 653 | |a Agriculture | ||
| 653 | |a Vegetation | ||
| 653 | |a Parameter identification | ||
| 653 | |a Global climate | ||
| 653 | |a Precipitation | ||
| 653 | |a Spatial data | ||
| 653 | |a Groundwater | ||
| 653 | |a Land use | ||
| 653 | |a Literature reviews | ||
| 653 | |a Stream flow | ||
| 653 | |a Rain | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Dhupper, Renu |u Amity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh, Noida, India (GRID:grid.444644.2) (ISNI:0000 0004 1805 0217) | |
| 700 | 1 | |a Kumari, Maya |u Amity University Uttar Pradesh, Amity School of Natural Resources and Sustainable Development (ASNRD), Noida, India (GRID:grid.444644.2) (ISNI:0000 0004 1805 0217) | |
| 700 | 1 | |a Gupta, Anil Kumar |u Integrated Center for Adaptation Climate Change Disaster Risk Reduction and Sustainability (ICARS), IIT Roorkee-Greater Noida Extension Centre (GNEC), Greater Noida, India (GRID:grid.19003.3b) (ISNI:0000 0000 9429 752X); National Institute of Disaster Management (NIDM), Rohini, Delhi, India (GRID:grid.502755.0) | |
| 700 | 1 | |a Kumar, Deepak |u Texas Tech University, Atmospheric Sciences Group, Department of Geosciences, College of Arts & Sciences, Texas, USA (GRID:grid.264784.b) (ISNI:0000 0001 2186 7496) | |
| 773 | 0 | |t SN Applied Sciences |g vol. 7, no. 10 (Oct 2025), p. 1185 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3260580306/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3260580306/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3260580306/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |