Assessment of Wave Energy in the South China Sea Based on GIS Technology

Lin, Gang; Shao, Lisheng; Zheng, Chunmiao; Chen, Xiao Bin; Zeng, Li Feng; Liu, Fei; Li, Rong Bo; Shi, Wei

doi:10.1155/2017/1372578

Cited by 9 publications

(4 citation statements)

References 39 publications

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“…The study area for this analysis was covered within the South China Sea (Figure 4), where this ocean area is frequently battered by strong winds from both the Indian Ocean and the South China Sea. In addition, the South China Sea's ocean energy resources have great potential for exploitation [17]. In the context of co-clustering using spatio-temporal data, the majority of research has used the same algorithm which is Bregman Block Average Co-Clustering with I-divergence (BBAC_I).…”

Section: Data and Study Areamentioning

confidence: 99%

Spatio-Temporal Wave Pattern using Multi-dimensional Clustering Method for Exploring Ocean Energy Potential

Rohana

Yusof

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Wave is formed from the movement of air caused by pressure variations that make airflow move from high pressure toward places of low pressure. Understanding the wave patterns is challenging since it is highly changeable in space as they travel in variety of directions and heights. Wave are also changing over time especially during the monsoon seasons. Hence, to extract significant information from this highly changeable behaviour of wave this study has utilized a multi-dimensional clustering technique called co-clustering. This technique is able to cluster spatio-temporal data with similar behavior into spatial and temporal components simultaneously. To reveal the spatial and temporal patterns, an algorithm called Bregman Block Average co-clustering with I-divergence (BBAC_I) has been implemented for extracting wave patterns. In order to discover the wave behaviour, the extracted wave patterns were visualized in the form of heatmap that contain information of co-clusters; spatial clusters and temporal clusters dimensions. Then, both spatial and temporal clusters from the heatmap were transformed into geographical maps to depict the variation of wave patterns based on their individual dimension. From these maps, we could observe the distribution of 8 different group of clusters that representing the spatial wave patterns. Furthermore, 5 individual maps have been produced to depict the temporal wave patterns across the study area. Finally, the obtained maps were interpreted in the form of wave height which were found to be within 0.4 to 1.4 m heights. The wave height information can be used for identifying their potential for ocean energy harvesting along the coastal area. In generally, the generated spatio-temporal wave patterns from this study could aid Malaysian marine agencies to provide strategic planning for proposing future ocean energy in Malaysian coastal area.

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Section: Data and Study Areamentioning

confidence: 99%

Spatio-Temporal Wave Pattern using Multi-dimensional Clustering Method for Exploring Ocean Energy Potential

Rohana

Yusof

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Man et al 62 used geographic information technology to assess the potential of Hong Kong's solar energy and assist in energy policy decisions. Lin et al 63 used the remote-sensing technology and GIS technology to evaluate the wave energy of the South China Sea. Using time series data, Zhang et al 64 analyzed and evaluated the energy intensity, per capita GDP carbon emissions, the proportion of renewable energy, and the dependence on oil imports of China and the United States, which indicated the evolution of China-U.S. energy strategy.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of energy strategy pressure based on geographical information system

Feng

Liu

Zhaoyong

et al. 2019

Energy & Environment

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Many studies have used geographical information system tools on a small scale for the analysis of energy potential, energy planning, and energy consumption, but there has been no example of the use of geographical information system tools to study large-scale pressures on energy strategies. This paper presents an analysis and assessment of energy strategy pressure for most countries worldwide using geographical information system mapping techniques. The research first presents an index system and a model for the evaluation of energy strategy pressure. Then, with the aid of geographical information system tools, an analysis and an evaluation of global energy strategy pressure are carried out. The factors that influence the development of energy strategies together constitute the energy strategy pressure index system: these include economic, environmental, resource, science and technology, security, and spatial pressure factors. For each pressure factor, a number of indicators are considered. The spatial distribution of the global energy strategy pressure index is dominated by band-like features, which are consistent with the global distribution of population and cities, the map of the continents and the Chinese Belt and Road proposals.

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“…Cold events in wintertime have been widely noticed by both of scholars, media, and the public people in China, because they have great impacts on the socioeconomy, health, and even human lives (Liu, 1990;Ding et al, 2008;Peng and Bueh, 2011). For example, in 2008, a special cold event coupled with heavy rain, snow and icy conditions swept most of China, affecting all the sectors and aspects of people's lives thereof and inducing large amount of economic loss at the same time (Zhao et al, 2008;Wang et al, 2008;Hong et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Influence of Phase Variations of Madden-Julian Oscillation on Wintertime Large-Scale Cold Events in China

Feng¹,

Li²,

Wang³

et al. 2023

Tellus A: Dynamic Meteorology and Oceanography

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The station observations and reanalysis dataset are utilized to explore the effects of Madden-Julian Oscillation (MJO) on the cold extremes over the whole country of China, and the possible mechanisms from the perspective of the thermodynamics. Here, we focus on the principal modes of different phases of MJO in winter and their influences on the large scale cold events (LSCEs) that are identified firstly. The time evolution and the spatial features of LSCEs are checked for a basic insight of the LSCEs in China, of which the annual variability and regional differences are obvious among the chosen LSECs. In addition, the first (second) empirical orthogonal decomposition mode of MJO shows an opposite feature that positive values in Phases 1-3 (Phase 4-5) and negative values in Phases 5-7 (Phase 7-8), with the explanation of variance at 30.6% (26%). Furthermore, according to the threshold of ±1.5 in standardized time series of two principal components (PC1 and PC2), the events are chosen and clarified into four cases (+PC1, -PC1, +PC2 and -PC2). All the MJO-related cases present the increases of LSCEs but with regional and intensity differences. For the case of +PC1, the cold advection from higher latitudes transport to eastern Asia inducing negative temperature anomalies thereof. For the case of -PC1, besides the eastern Asian region, there still the cold advections across the Inner Mongolia regions, leaving negative anomalies over the region either. For the case of +PC2, the southward wind and the accompanied cold advection are stronger than the others affecting mostly regions in China, which leads to the more decreases of temperature. For the case of -PC2, the cold advections are weaker, resulting in the less temperature decreases over the southeastern China. Meanwhile, the tropospheric cyclonic and anti-cyclonic circulation anomalies are beneficial to the persistence of local extremes.

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Assessment of Wave Energy in the South China Sea Based on GIS Technology

Cited by 9 publications

References 39 publications

Spatio-Temporal Wave Pattern using Multi-dimensional Clustering Method for Exploring Ocean Energy Potential

Spatio-Temporal Wave Pattern using Multi-dimensional Clustering Method for Exploring Ocean Energy Potential

Assessment of energy strategy pressure based on geographical information system

Influence of Phase Variations of Madden-Julian Oscillation on Wintertime Large-Scale Cold Events in China

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