利用马尔可夫链和细胞自动机算法估算开恩寺水库未来的测深面

IF 2.3 Q2 REMOTE SENSING
Pius Onoja Ibrahim, Harald Sternberg, Lazarus Mustapha Ojigi
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引用次数: 0

摘要

沉积物对水库的威胁对水质、蓄水能力和水库寿命都有重大影响。降雨模式及其他人为和环境影响会改变侵蚀率,进而直接影响沉积率。这项研究的重点是利用马尔可夫链和细胞自动机(MC-CA)模型的整合,估算和预测尼日利亚 Kainji 水库 2050 年的未来水深表面。本研究使用的测深数据集包括两个不同的年代(1990 年和 2020 年)。数据集是使用单波束回声测深仪以 20 千赫和 200 千赫的低频和高频采集的。初步调查显示,沉积加剧了对水库功能的威胁。结果表明,观测到的最大深度为 71.2 米,与 1990 年的存档数据相比,深度损失了 7.53%,从 1968 年到 2020 年,沉积造成的深度损失为 16.24%,2050 年的深度损失为 22.35%,如预测表面所示。因此,根据数据特征,综合模型(MC 和 CA)有效地预测了 2050 年开恩寺水库的未来水深表面。不过,最适合分析分类过渡数据的马尔可夫链和细胞自动机等成熟的空间数据分析技术,在预测生成的地图上出现了一些假象(黑点),有待进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating future bathymetric surface of Kainji Reservoir using Markov Chains and Cellular Automata algorithms

The menace of sedimentation to reservoirs has a significant implication for water quality, storage capacity and reservoir lifetime. Rainfall patterns and other anthropogenic and environmental impacts alter the erosion rate and, by extension, directly affect sedimentation rates if left unchecked. This research focused on using the integration of Markov Chains and Cellular Automata (MC – CA) models to estimate and forecast the future bathymetric surface of the Kainji reservoir in Nigeria for the year 2050. The bathymetric datasets used for this research comprise two different epochs (1990 and 2020). The datasets were acquired using a Single Beam Echosounder at Low and High frequencies of 20 kHz and 200 kHz. The preliminary investigation revealed that sedimentation is exacerbating a greater danger to the reservoir functionality. The results show that the maximum observed depth is 71.2 m, indicating a 7.53% loss in depth from the 1990 archived data and a 16.24% depth loss to sedimentation from 1968 to 2020 and 22.35% depth loss in the year 2050 as shown on the projected surface. Consequently, the integrated model (MC and CA) efficiently predicted the future bathymetric surface of the Kainji reservoir for the year 2050 based on the data characteristics. However, the proven techniques for analysing spatial data, such as the Markov Chain and Cellular Automata, best suited for analysing categorical transition data, show some artefacts (black spots) on the projected generated map which is subject to further investigation.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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