id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-306124-sn780ike	Jakariya, Md.	Assessing climate-induced agricultural vulnerable coastal communities of Bangladesh using machine learning techniques	2020-06-16		.txt	text/plain	4077	193	43	The study also identified the need for assessing vulnerability after certain intervals, specifically owing to the dynamic nature of the coastal region where the factors were found to vary among the different study areas. An effort was made to find the crop yield vulnerability of the farmers of the three coastal districts of Bangladesh by identifying the significant factors that have increased effects on the vulnerability score by Machine Learning models. The factors related to three different variables of vulnerability, e.g., exposure, sensitivity, and adaptive capacity, were identified through focus group discussions (FGD) with the local farmers in each village. Across the coastal region of Bangladesh, the climatic conditions were amongst the factors with the highest weights, which illustrate their importance to assess vulnerability levels. Table 3 shows the state of crop yield vulnerability of the three coastal regions of Bangladesh, which is reflected in the vulnerability scores of different villages in the study area.	./cache/cord-306124-sn780ike.txt	./txt/cord-306124-sn780ike.txt