气候变化领域集成服务门户(CCPortal): Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms

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DOI	10.1016/j.scitotenv.2023.161394
	Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms
	Abdulla-Al Kafy; Bakshi, Arpita; Saha, Milan; Al Faisal, Abdullah; Almulhim, Abdulaziz I.; Rahaman, Zullyadini A.; Mohammad, Pir
发表日期	2023
ISSN	0048-9697
EISSN	1879-1026
卷号	867
英文摘要	The consequences of droughts are far-reaching, impacting the natural environment, water quality, public health, and accelerating economic losses. Applications of remote sensing techniques using satellite imageries can play an influen-tial role in identifying drought severity (DS) and impacts for a broader range of areas. The Barind Tract (BT) is a region of Bangladesh located northwest of the country and considered one of the hottest, semi-arid, and drought-prone regions. This study aims to assess and predict the drought vulnerability over BT using Landsat satellite images from 1996 to 2031. Several indices, including Normalized Difference Vegetation Index (NDVI), Modified Normalized Differ-ence Water Index (MNDWI), Soil Moisture Content (SMC), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). VHI has been used to identify and predict DS based on VCI and TCI characteristics for 2026 and 2031 using Cellular Automata (CA)-Artificial Neural Network (ANN) algorithms. Results suggest an increasing patterns of DS accelerated by the reduction of healthy vegetation (19 %) and surface water bodies (26 %) and increased higher temperature (>5 degrees C) from 1996 to 2021. In addition, the VHI result signifies a mas-sive increase in extreme drought conditions from 1996 (2 %) to 2021 (7 %). The DS prediction witnessed a possible expansion in extreme and severe drought conditions in 2026 (15 % and 13 %) and 2031 (18 % and 24 %). Understand-ing the possible impacts of drought will allow planners and decision-makers to initiate mitigating measures for enhanc-ing the communities preparedness to cope with drought vulnerability.
英文关键词	Drought; Vegetation health; Temperature; Water resources; Machine learning
语种	英语
WOS研究方向	Environmental Sciences
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000921070000001
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281528
作者单位	University of Texas System; University of Texas Austin; Khulna University of Engineering & Technology (KUET); Independent University Bangladesh (IUB); Bangladesh University of Engineering & Technology (BUET); McGill University; Imam Abdulrahman Bin Faisal University; Universiti Pendidikan Sultan Idris; Hong Kong Polytechnic University
推荐引用方式 GB/T 7714	Abdulla-Al Kafy,Bakshi, Arpita,Saha, Milan,et al. Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms[J],2023,867.
APA	Abdulla-Al Kafy.,Bakshi, Arpita.,Saha, Milan.,Al Faisal, Abdullah.,Almulhim, Abdulaziz I..,...&Mohammad, Pir.(2023).Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms.SCIENCE OF THE TOTAL ENVIRONMENT,867.
MLA	Abdulla-Al Kafy,et al."Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms".SCIENCE OF THE TOTAL ENVIRONMENT 867(2023).