STAO: a component architecture for raster and time series modeling

Babu, A. Jagadeesh; Thirumalaivasan, D.; Venugopal, K R

doi:10.1016/j.envsoft.2004.11.011

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…Furthermore, COM encapsulates all properties and methods internally and can only be exposed to the outside by several well-defined interfaces. The high encapsulation of COM essentially allows the components to become self-contained building blocks that can be assembled into larger systems (e.g., [23]) and be reused at the binary level; thus, third-party developers do not require source codes or object libraries in order to extend the system (e.g., [29]). As the core technology of ArcGIS platform, COM has been applied to nearly all aspects of the software, such as graphical user interface (GUI), display module, and geometry module, making it extendible.…”

Section: Arcgis and Com Technologymentioning

confidence: 99%

“…In contrast to the other integration strategies, the development of the PIHMgis framework is notable in the sense that it introduced an advanced coupling strategy, which was achieved by a shared data model between open source GIS (QGIS, http://www.qgis.org accessed on 3 November 2023) and Penn State Integrated Hydrologic Model (PIHM) (e.g., [5,21,22]). Obviously, data access is the first challenge in integrating GIS and hydrological models (e.g., [23]). Due to the different objectives and design concepts, differences between data structures in hydrological models and GIS are remarkable.…”

Section: Introductionmentioning

confidence: 99%

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Seamlessly Coupling Hydrological Modelling Systems and GIS through Object-Oriented Programming

Xiang,

Pan,

et al. 2023

JMSE

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Coupling hydrological modelling systems (HMS) with a geographic information system (GIS) can significantly enhance hydrological research and expand its applications. The calculation for HMS requires geographic information data; however, the current GIS data structure is not equipped to support the object-oriented hydrological modelling. Due to different objectives and design concepts, the differences between HMS and GIS have been profound, especially in their data structures from the perspective of object-oriented programming (OOP). This research introduces a novel approach to extend ArcGIS data structures for HMS, facilitating seamless coupling. This approach employs Microsoft Component Object Model (COM) technology to construct custom data structures that align with hydrological OOP principles. These can then be integrated into ArcGIS through a custom ArcGIS layer as an add-on. As a result, the HMS can leverage the full functionality of ArcGIS without the need for re-coding existing modelling systems. Moreover, HMS can be readily developed by using COM compatible computer languages, enabling the easy adaptation of this coupling approach to other HMS to ensure computational efficiency and to maximise the benefits of ArcGIS features. This new approach has been successfully implemented with the Xin’anjiang model, and the results validate its effectiveness in coastal areas.

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Section: Arcgis and Com Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seamlessly Coupling Hydrological Modelling Systems and GIS through Object-Oriented Programming

Xiang,

Pan,

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…Specialized software is necessary for processing time series of digital images for decision-making in agriculture, capable of handling both one-dimensional and multi-dimensional time series data at a high level [2]. Another limitation of modern geoinformation systems and software packages for image processing and pattern recognition is that these systems do not provide all the necessary functions for time series processing and require data storage arrays for image access capabilities, i.e., they do not offer optimization for efficient monitoring of the state of agricultural crop areas [3].…”

Section: Introductionmentioning

confidence: 99%

Development of a yield monitoring model based on analysis of surveys and images of the field

Huang

2024

MDCS

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The study is devoted to the construction of a mathematical model of the yield of agricultural crops, which includes three components: trend, seasonal, and random. The developed model shows the dependence of yield on phenological indicators, the quality of land resources, management efficiency, and other random factors. The trend and seasonal components of the yield model do not depend on random factors and can therefore be used to predict yield. It is proposed to consider the trend component of the model as a piecewise linear function, and the seasonal component of the model as a linear harmonic regression. A method for analyzing multispectral images with consideration of geoinformation data has been developed to assess phenological indicators. This method includes determining the threshold value using Otsu method to find the density of the agricultural crop in the field. Data on crop density, supplemented with geodata about the plot boundaries, are used to calculate the yield. A comparison of yield forecasts for three crops in the Chernihiv region was made using observations of the phenological indicators of crops throughout the year and over 3 months. It was found that yield is significantly determined by plant development in the first months after germination. The comparison of yield forecasts was made with data from the State Statistics Service of Ukraine and forecasts made using the WOFOST simulation model. It was established that the average relative error of yield prediction using the developed model is 2.96% when using observations of the phenological indicators of crops throughout the year and 4.51% when observing over 3 months. This accuracy is sufficient and comparable to the average accuracy of predictions based on the WOFOST model, which is 3.62%).

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“…Coupling between models and GIS was described as loose, tight, and embedded (Jagadeesh et al 2006). However, in most cases the GIS is used to process and display inputs and outputs to an external model that can be started automatically via scripting, or manually (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…One type is adding a simple GIS to a complex modeling system to display results and provide interactive control; another is where the model is written using the analytical engine of the GIS. Though simple models can be developed in the analytical engine of GIS, testing and debugging of source code in this environment is difficult and most of the GIS analytical engines are interpreted not compiled and also execution is very slow (Jagadeesh et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Integrating classification and regression tree (CART) with GIS for assessment of heavy metals pollution

Cheng

Zhang

Wang

et al. 2008

Environ Monit Assess

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The classification and regression tree (CART) model integrated with geographical information systems and the assessment of heavy-metals pollution system was developed to assess the heavy metals pollution in Fuyang, Zhejiang, China. The integration of the decision tree model with ArcGIS Engine 9 using a COM implementation in Microsoft Visual Basic 6.0 provided an approach for assessing the spatial distribution of soil Zn content with high predictive accuracy. The Zn concentration classes estimated by CART assigned the right classes with an accuracy of near 90%. This is a great improvement compared to the ordinary Kriging method for the spatial autocorrelation of the study area severely destroyed by human activities. Also, it can be used to investigate the inter-relationships between the heavy metals pollution and environmental and anthropogenic variables. Moreover, the research presents model predictions over space for further applications and investigations.

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STAO: a component architecture for raster and time series modeling

Cited by 7 publications

References 17 publications

Seamlessly Coupling Hydrological Modelling Systems and GIS through Object-Oriented Programming

Seamlessly Coupling Hydrological Modelling Systems and GIS through Object-Oriented Programming

Development of a yield monitoring model based on analysis of surveys and images of the field

Integrating classification and regression tree (CART) with GIS for assessment of heavy metals pollution

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