Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Yaser A. Nanehkaran"'
Publikováno v:
Environmental Sciences Europe, Vol 36, Iss 1, Pp 1-14 (2024)
Abstract Background Microplastic pollution is a pressing issue with far-reaching environmental and public health consequences. This study delves into the intricacies of predicting microplastic pollution during the COVID-19 pandemic in Tehran, Iran. M
Externí odkaz:
https://doaj.org/article/2694068724f442669aff6d833f1bd157
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-20 (2023)
Abstract Azarshahr County in the northwest of Iran is predominantly covered by Azarshahr travertine, a prevailing sedimentary rock. This geological composition has led to extensive open-pit mining activities, particularly in the western and southwest
Externí odkaz:
https://doaj.org/article/441ac7f58930423b96315eb8dde98d4d
Autor:
Yaser A. Nanehkaran, Zhu Licai, Mohammad Azarafza, Sona Talaei, Xu Jinxia, Junde Chen, Reza Derakhshani
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in envi
Externí odkaz:
https://doaj.org/article/6b9cf92d417a4512bf1d23ef216645c1
Publikováno v:
Frontiers in Environmental Science, Vol 11 (2023)
Landslide susceptibility mapping (LSM) is a crucial step during landslide assessment and environmental management. Clustering algorithms can construct effective models for LSM. However, a random selection of important parameters, inconsideration of u
Externí odkaz:
https://doaj.org/article/e4ba28b3a7824d59abe401c31f87f302
Autor:
Ahmed Cemiloglu, Licai Zhu, Agab Bakheet Mohammednour, Mohammad Azarafza, Yaser Ahangari Nanehkaran
Publikováno v:
Land, Vol 12, Iss 7, p 1397 (2023)
Landslide susceptibility assessment is the globally approved procedure to prepare geo-hazard maps of landslide-prone areas, which are highly used in urban management and minimizing the possible disasters due to landslides. Multiple approaches to prov
Externí odkaz:
https://doaj.org/article/7da8ee2af2c148e2986f681d79f4ff8c
Publikováno v:
IET Image Processing, Vol 15, Iss 5, Pp 1115-1127 (2021)
Abstract Crop diseases have a devastating effect on agricultural production, and serious diseases can lead to harvest failure entirely. Recent developments in deep learning have greatly improved the accuracy of image identification. In this study, we
Externí odkaz:
https://doaj.org/article/e6267d42e9f6414f9615f7e362a0199d
Autor:
Yaser A. Nanehkaran, Zhu Licai, Jin Chengyong, Junde Chen, Sheraz Anwar, Mohammad Azarafza, Reza Derakhshani
Publikováno v:
Applied Sciences, Vol 13, Iss 3, p 1555 (2023)
Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S) pl
Externí odkaz:
https://doaj.org/article/d4ef786bbf7e42b3a98403aa31c809c0
Publikováno v:
IEEE Access, Vol 7, Pp 127956-127966 (2019)
The economic operation of power transformers is analyzed in the present paper, which is performed by the clustering analysis method. In order to overcome the disadvantages of the conventional k-means algorithm lacking the stability and accuracy, we p
Externí odkaz:
https://doaj.org/article/dd88ba0151e546ebbe1b79738a1b481d
Autor:
Mao Yimin, Li Yican, Deborah Simon Mwakapesa, Wang Genglong, Yaser Ahangari Nanehkaran, Muhammad Asim Khan, Zhang Maosheng
Publikováno v:
Advances in Civil Engineering, Vol 2021 (2021)
This study aims at proposing and designing an improved clustering algorithm for assessing landslide susceptibility using an integration of a Chameleon algorithm and an adaptive quadratic distance (CA-AQD algorithm). It targets improving the predictio
Externí odkaz:
https://doaj.org/article/ae1cb3abad8249fd93b73367691cfa7b
Publikováno v:
Sustainability; Volume 15; Issue 5; Pages: 4218
Landslide susceptibility mapping (LSM) studies provide essential information that helps various authorities in managing landslide-susceptible areas. This study aimed at applying and comparing the performance of DIvisive ANAlysis (DIANA) and RObust Cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d5fa142b1abdc68a7a4ec61c6ae37228