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Titlebook: Innovation in Smart and Sustainable Infrastructure; Select Proceeding of Dhruvesh Patel,Byungmin Kim,Dawei Han Conference proceedings 2024

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41#
發(fā)表于 2025-3-28 16:40:34 | 只看該作者
Conference proceedings 2024 The contents focus on smart infrastructure and cites, construction and infrastructure project management, application of building information modelling, sustainable materials and methods for road construction, smart technologies, applications and services for transportation systems, remote sensing
42#
發(fā)表于 2025-3-28 20:47:14 | 只看該作者
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發(fā)表于 2025-3-29 02:18:09 | 只看該作者
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發(fā)表于 2025-3-29 05:04:44 | 只看該作者
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發(fā)表于 2025-3-29 08:52:43 | 只看該作者
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發(fā)表于 2025-3-29 14:45:49 | 只看該作者
47#
發(fā)表于 2025-3-29 18:32:16 | 只看該作者
A Dam Break Analysis of Damanganga Dam Using HEC-RAS 2D Hydrodynamic Modelling and Geospatial Techni) 2D hydrodynamic modelling and geospatial techniques. Inflow hydrograph, Digital Elevation Model (DEM), topography, and area capacity curve of Damanganga reservoir have been utilized to build a 2D hydrodynamic model. Froehlich equation was used to estimate dam breach parameters. Water surface eleva
48#
發(fā)表于 2025-3-29 21:03:05 | 只看該作者
Enhancing the Optimum Water Requirement for the Crop Using the Contemporary Capillary Wick Irrigatioof the irrigator for its effective use. The present experimental study seeks to examine the applicability of the proposed innovative indigenous ‘capillary wick irrigation technique’ through a small underground water reservoir for row crops by comparing the same with the farmers’ traditional method (
49#
發(fā)表于 2025-3-30 01:47:58 | 只看該作者
A Comparative Assessment of Unsupervised and Supervised Methodologies for LANDSAT 8 Satellite Image elihood algorithm delivered the highest classification accuracy (73.8%), followed by SAM (70.7%), MD (68.1), K means (41.5%) and ISODATA (31.2%) algorithms. Further, accuracy enhancements are attained by the classification sieve filter (83.2%) and by applying manual corrections (89.7%).
50#
發(fā)表于 2025-3-30 07:50:01 | 只看該作者
Comparison of Image Processing Techniques to Identify the Land Use/Land Cover Changes in the Indian re training file is prepared using the pixel values of the image and its properties in the ArcGIS 10.1. Then, the supervised classification was done using the maximum likelihood classifier in 4 different classes (Class 1: Vegetation, Class 2: Barren land, Class 3: Waterbody, and Class 4: Built-up).
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