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Assessment of Urban Physical Seismic Vulnerability Using the Combination of AHP and TOPSIS Models: A Case Study of Residential Neighborhoods of Mymensingh City, Bangladesh 被引量:1
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作者 Md. Shaharier Alam Shamim Mahabubul Haque 《Journal of Geoscience and Environment Protection》 2018年第2期165-183,共19页
Mymensingh, one of the oldest municipalities of Bangladesh, is at great risk against earthquakes because three major faults viz. Dauki fault, Madhupur fault, and Sylhet-Assam fault are located around it, and possesses... Mymensingh, one of the oldest municipalities of Bangladesh, is at great risk against earthquakes because three major faults viz. Dauki fault, Madhupur fault, and Sylhet-Assam fault are located around it, and possesses liquefaction susceptible soil type. The city has great significance from the economic and administrative point of view and recently declared as the 8th administrative division of Bangladesh which directly stimulates the unplanned future expansion. Considering the potentiality of haphazard development and high seismic risk, it is crucial to assess the seismic vulnerability for taking the judicious decision regarding risk reduction measures for the city. The study combines Technique for Order Preference by Similarity to Ideal Solution method (TOPSIS) and Analytical Hierarchical Process (AHP) models to assess the seismic vulnerability of residential neighborhoods of Mymensingh city as the probability of death and damage is remarkable in residential neighborhoods than other land use types. A combined quantitative methodology of AHP-TOPSIS is used in this study to quantify 13 important qualitative and quantitative factors of earthquake vulnerability, decided on expert opinions. The data of 13 vulnerability factors are collected from the Mymensingh Strategic Development Plan (MSDP, 2011-2031) database, done under Comprehensive Disaster Management Programme (CDMP)-II during 2012-2014. Geographic Information System is used in this study to analyze and mapping of seismic vulnerability. Results indicated that 37 residential neighborhoods are very highly vulnerable, 55 neighborhoods are highly vulnerable, 75 neighborhoods are moderately vulnerable and 74 neighborhoods are in the low vulnerable category. 展开更多
关键词 AHP GIS PHYSICAL SEISMIC VULNERABILITY TOPSIS
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Floristic composition and management of cropland agroforest in southwestern Bangladesh
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作者 Md. Hasanuzzaman Mahmood Hossain Mustafa Saroar 《Journal of Forestry Research》 SCIE CAS CSCD 2014年第3期597-604,共8页
Cropland agroforest is an important production system in the southwest region of Bangladesh. This study focused on the floristic composition and management of existing cropland agroforests. A total of 313 cropland agr... Cropland agroforest is an important production system in the southwest region of Bangladesh. This study focused on the floristic composition and management of existing cropland agroforests. A total of 313 cropland agroforests were surveyed and 83%respondents practiced pure agroforestry while the remaining 17% practiced agroforestry with fisheries. A total of 18 forest trees and 2 shrubs were recorded from 11 families and 59 species of agricultural crops were from 28 families. A higher proportion (79%) of cropland agroforests were occupied small land areas (0.12-0.80 ha). About 63% of respondents planted trees for fruit production and 47%for timber production, and 35%of respondents engaged in commercial production (35%). Swietenia macrophylla was the most prevalent species (relative prevalence 20.83) followed by Man-gifera indica (relative prevalence 15.57) and Cocos nucifera (relative prevalence 7.08). Shorter spacing was used for timber and fuel wood species and wider spacing for fruit trees. A wide range of rotation periods, from 5 to 25 years, was observed for both cases. The use of chemical fertilizer was highest followed by cow dung and compost in cropland agroforests. Overall management practices of cropland agroforest in southwest Bangladesh were determined by the end product and local demand. 展开更多
关键词 AGROFORESTRY BANGLADESH CROPLAND fruit production timber production
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