Zobrazeno 1 - 10 of 115 pro vyhledávání: '"Kalantar, B"'
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3
Object-Oriented Approach for Urbanization Growth by Using Remote Sensing and GIS Techniques: A Case Study in Hilla City, Babylon Governorate, Iraq
Autor: Mahmoud, AS, Kalantar, B, Al-Najjar, HAH, Moayedi, H, Halin, AA, Mansor, S
High rate of urbanization coupled with population growth has led to unexpected land use and land cover changes in Hilla city, which is located in the Babylon governorate of Iraq. Understanding and quantifying the spatiotemporal dynamics of the urban
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::bbfb61b81f890ef1c027e68ccc62d051
https://hdl.handle.net/10453/158447
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4
Assessment of convolutional neural network architectures for earthquake-induced building damage detection based on pre-and post-event orthophoto images
Autor: Kalantar, B, Ueda, N, Al-Najjar, HAH, Halin, AA
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::12d62d377fa39f5a8cb42b85a3d46aa9
https://hdl.handle.net/10453/145003
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6
A Novel Swarm Intelligence—Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility
Autor: Bui, DT, Moayedi, H, Kalantar, B, Osouli, A, Pradhan, B, Nguyen, H, Rashid, ASA
Publikováno v: Sensors, Vol 19, Iss 16, p 3590 (2019)
Sensors (Basel, Switzerland)
In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performan
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::4189b746c6ea195970d95d4cace2e6cd
https://www.mdpi.com/1424-8220/19/16/3590
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7
Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential
Autor: Naghibi, SA, Dolatkordestani, M, Rezaei, A, Amouzegari, P, Heravi, MT, Kalantar, B, Pradhan, B
© 2019, Springer Nature Switzerland AG. Groundwater resources are facing a high pressure due to drought and overexploitation. The main aim of this research is to apply rotation forest (RTF) with decision trees as base classifiers and an improved ens
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::09dd69e09240f8513cdc5632aa36bbaf
https://hdl.handle.net/10453/134885
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8
Groundwater potential mapping using a novel data-mining ensemble model
Autor: Kordestani, MD, Naghibi, SA, Hashemi, H, Ahmadi, K, Kalantar, B, Pradhan, B
© 2018, The Author(s). Freshwater scarcity is an ever-increasing problem throughout the arid and semi-arid countries, and it often results in poverty. Thus, it is necessary to enhance understanding of freshwater resources availability, particularly
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::8d92478ef66373474d3fd303bcb9e770
https://hdl.handle.net/10453/130841
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9
Optimized conditioning factors using machine learning techniques for groundwater potential mapping
Autor: Kalantar, B, Al-Najjar, HAH, Pradhan, B, Saeidi, V, Halin, AA, Ueda, N, Naghibi, SA
© 2019 by the authors. Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis me
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::a7e42dd9d9fcfdb59e333b864eb11dac
https://hdl.handle.net/10453/139960
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10
Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms
Autor: Al-Najjar, HAH, Kalantar, B, Pradhan, B, Saeidi, V
© 2019 SPIE. Landslides are type of natural geohazard interfering with many economical and social activities and causing serious damages on human life. It is ranked as a great disaster, threatening life, property and environment. Therefore, early pr
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::c6716445ae561b9a4b959fea7709ca97
https://hdl.handle.net/10453/141601
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