Spatio-temporal wave pattern using multi-dimensional clustering method for exploring ocean energy potential

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

Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications

Condition monitoring is the process of monitoring parameters expressing machinery condition, interpreting them for the identification of change which could indicate developing faults. Data processing is important in a ship condition monitoring software tool, as misinterpretation of data can significantly affect the accuracy and performance of the predictions made. Data for key performance parameters for a PANAMAX container ship main engine cylinder are clustered using a two-stage approach. Initially, the data is clustered using the artificial neural network (ANN)-self-organising map (SOM) and then the clusters are interclustered using the Euclidean distance metric into groups. The case study results demonstrate the capability of the SOM to monitor the main engine condition by identifying clusters containing data which are diverse compared to data representing normal engine operating conditions. The results obtained can be further expanded for application in diagnostic purposes, identifying faults, their causes and effects to the ship main engine

Advancing Spatiotemporal Research of Visitor Travel Patterns Within Parks and Protected Areas

Recent technological advances have made it possible to more accurately understand visitor travel patterns and their associated impacts. These advancements help to: accumulate voluminous data sets, collect alternative location data similar to GPS data, conduct spatiotemporal inferential statistics, and advance spatiotemporal visualizations. However, investigations of visitor travel patterns have not kept pace with recent technological advancements. Therefore, the purpose of this dissertation was to advance spatiotemporal research of visitor travel patterns within parks and protected areas by leveraging new technologies. The studies reported in this dissertation were designed to begin filling this gap, and include results from research conducted at: 1) Theodore Roosevelt National Park to identify which spatiotemporal variables are the most important to managers for understanding visitor travel patterns; 2) Hawai’i Volcanoes National Park to identify air tour travel patterns; and 3) the Bonneville Salt Flats to understand visitor travel patterns in a dispersed recreation setting that lacks organizational infrastructure. These three independent but conceptually linked studies were designed to inform our understanding of visitor travel patterns within parks and protected areas. This information is important so that park managers: a) understand how space and time influence visitor routes; and b) have relevant information to continue to conserve the biophysical resource while providing opportunities for quality visitor experiences. Results from the study at Theodore Roosevelt National Park showed that managers identified three temporal variables as being the most important towards understanding visitor travel patterns. These variables were analyzed to determine time allocation and vehicle speed patterns. Results from the study at Hawai’i Volcanoes National Park determined air tour travel patterns and which terrestrial attraction areas were the most affected by air tours. The study at the Bonneville Salt Flats identified potential areas of conflict and designed areas recommended for monitoring. Overall, this dissertation contributes to further understanding of visitor travel patterns, which provides information for managers to continue conserving parks and protected areas for the benefit of society

Geovisualization of Mountain Hydroclimatic Variability: Linkages Between Atmospheric Circulation and the Spatial Pattern of Precipitation

The hydroclimatology of mountain regions is important for the world’s water resources, yet is poorly understood in the context of climate variability and change. Observations indicate that drought and heavy precipitation have increased throughout the mid-latitudes during the last century, and future projections show a continuation of these trends. Despite these predictions, our understanding of the physical processes and associated spatial patterns of precipitation in mountains is lacking. In order to make better predictions of hydroclimatic change in mountain regions, research is first needed to understand the relationship between atmospheric circulation and precipitation. The purpose of this dissertation is to examine this relationship in the southern Appalachian Mountains of the southeastern United States, a mid-latitude mountain region that exhibits much hydroclimatic variability. I use both established and innovative geovisualization techniques to reveal how topographic features mediate the relationship between large scale circulation and precipitation characteristics. First, I compare several types of statistical clustering algorithms to identify hydroclimatic regions based on commonalities in the type and frequency of summer rainfall. Second, a self-organizing map is used to classify and visualize patterns of synoptic-scale circulation variability. The identified patterns are then linked with daily precipitation characteristics across the different hydroclimatic and topographic regions. To increase the interpretability of the relationships between circulation and precipitation, I develop a new graphical representation for displaying simple spatial statistics of the precipitation characteristics across the self-organizing map output space. This research identifies areas across the landscape that are most susceptible and most resilient to extreme hydroclimatic events. Light precipitation events are most frequently observed across the highest elevations and plateaued regions, no matter which circulation pattern is present. In contrast, moderate and heavy precipitation events occur most frequently along the Blue Ridge and foothills regions, and are limited to a small subset of the circulation patterns. Ultimately, this dissertation shows how shifts in the large scale circulation, which are expected with climate change, are likely to alter the spatial footprint of precipitation events and their extremes across mountain catchments.Doctor of Philosoph

Hierarchical self-organizing maps for clustering spatiotemporal data

Spatial sciences are confronted with increasing amounts of high-dimensional data. These data commonly exhibit spatial and temporal dimensions. To explore, extract, and generalize inherent patterns in large spatiotemporal data sets, clustering algorithms are indispensable. These clustering algorithms must account for the distinct special properties of space and time to outline meaningful clusters in such data sets. Therefore, this research develops a hierarchical method based on self-organizing maps. The hierarchical architecture permits independent modeling of spatial and temporal dependence. To exemplify the utility of the method, this research uses an artificial data set and a socio-economic data set of the Ostregion, Austria, from the years 1961 to 2001. The results for the artificial data set demonstrate that the proposed method produces meaningful clusters that cannot be achieved when disregarding differences in spatial and temporal dependence. The results for the socio-economic data set show that the proposed method is an effective and powerful tool for analyzing spatiotemporal patterns in a regional context

Hierarchical self-organizing maps for clustering spatiotemporal data

Spatial sciences are confronted with increasing amounts of high-dimensional data. These data commonly exhibit spatial and temporal dimensions. To explore, extract, and generalize inherent patterns in large spatiotemporal data sets, clustering algorithms are indispensable. These clustering algorithms must account for the distinct special properties of space and time to outline meaningful clusters in such data sets. Therefore, this research develops a hierarchical method based on self-organizing maps. The hierarchical architecture permits independent modeling of spatial and temporal dependence. To exemplify the utility of the method, this research uses an artificial data set and a socio-economic data set of the Ostregion, Austria, from the years 1961 to 2001. The results for the artificial data set demonstrate that the proposed method produces meaningful clusters that cannot be achieved when disregarding differences in spatial and temporal dependence. The results for the socio-economic data set show that the proposed method is an effective and powerful tool for analyzing spatiotemporal patterns in a regional context