Geographic modeling and simulation systems for geographic research in the new era: Some thoughts on their development and construction

Chen, Min; Lv, Guonian; Zhou, Chenghu; Lin, Hui; Ma, Zaiyang; Yue, Songshan; Wen, Yongning; Zhang, Fengyuan; Wang, Jin; Zhu, Zuo-Nong; Xu, Kai; He, Yuanqing

doi:10.1007/s11430-020-9759-0

Cited by 29 publications

(7 citation statements)

References 119 publications

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“…The geo‐analysis activity implementation module manages different web services. Currently, the services managed in this module are from OpenGMS (Open Geographic Modeling and Simulation Systems) model service containers and data service containers that can provide a variety of geographic model and data processing services (Chen et al, 2020; Chen, Lv, et al, 2021; Wang et al, 2018; Wang et al, 2020; Wen et al, 2017; Yue et al, 2016; Zhang et al, 2019; Zhang, Chen, et al, 2020). The module can configure the input data and parameters, as well as invoke a specific external service for data processing and geographic phenomena simulation.…”

Section: Geo‐analysis Activity Implementation Modulementioning

confidence: 99%

A process‐driven geo‐analysis engine to support online collaborative geographic experiments

Chen

Zheng

et al. 2022

Transactions in GIS

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Geographic experiments, as a comprehensive research approach, are utilized to explore natural and social worlds. To help geographically dispersed people engage in geographic experiments and improve experimental outcomes, online collaborative geographic experiments have received increasing attention. As a new generation of geo‐analysis tools, VGEs (virtual geographic environments) can provide computer‐aided tools to serve geographic experiments. They can not only provide the collaborative GIS capabilities, but also implement geographic simulations using different cyberspace dispersed geographic models. For the complicated and interaction‐intensive online collaborative geographic experiments, VGEs also need to help experts implement geographic experiments following a well‐defined process. Nevertheless, the process is hardly considered in the existing support systems of collaborative geographic experiments. Therefore, we clarify the categories and relationships of interactions during the process of collaborative geographic experiments and design a process‐driven geo‐analysis engine. Through this engine, participants can maintain the processes of their collaborative geographic experiments, as well as implement interactions throughout the entire process. To verify the capability of the engine, it is used to support the case study on traffic noise simulation. The results suggest that the engine could assist the different geo‐analysis applications with a well‐defined process.

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Section: Geo‐analysis Activity Implementation Modulementioning

confidence: 99%

A process‐driven geo‐analysis engine to support online collaborative geographic experiments

Chen

Zheng

et al. 2022

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“…The mixed clustering method in this paper may cause the problem of classification redundancy, which needs to be improved in the future. Moreover, comprehensive geographic research usually focuses on various geographic elements to explore the relationships and interactions of elements behind geographic phenomena and processes [43]; the results of this article show that meteorological data are very important for predicting GWL, and it will be a very good research method if these data can be coupled with meteorological forecast models and used in the modeling.…”

Section: Effect Of Spatial Connectivity On Model Accuracymentioning

confidence: 99%

A ConvLSTM Conjunction Model for Groundwater Level Forecasting in a Karst Aquifer Considering Connectivity Characteristics

Guo

Li²,

Li³

et al. 2021

Water

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Groundwater is an important water resource, and groundwater level (GWL) forecasting is a useful tool for supporting the sustainable management of water resources. Existing studies have shown that GWLs can be accurately predicted by combining an artificial neural network model with meteorological and hydrological factors. However, GWL data are typically geographic spatiotemporal series data, and current studies have considered only the spatial distance factor when predicting GWLs. In karst aquifers, the GWL is affected by the developmental degree of the karst, topographic factors, structural features, and other factors; considering only the spatial distance is not enough, and the real spatial connectivity characteristics need to be considered. Thus, in this paper, we proposed a new method for forecasting GWLs in karst aquifers while considering connectivity characteristics using a neural network prediction model. The connectivity of a karst aquifer was analyzed by a multidimensional feature clustering method based on the distance index and hydrogeological characteristics recorded at observation wells, and a convolutional long short-term memory (ConvLSTM) conjunction model was constructed. The proposed approach was validated through GWL simulations and predictions in karst aquifers in Jinan, China, and four experiments were conducted for comparison. The experimental results show that the proposed method provided the most consistent results with the measured observation well data among the analyzed methods. These findings demonstrate that the proposed method, which considers connectivity characteristics in karst aquifers, has a higher simulation accuracy than other methods. This method is therefore effective and provides a new idea for the real-time prediction of the GWLs of karst aquifers.

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“…To construct a numerical model for the simulation of geographic processes [5,6], the first task is to choose a desirable discrete grid. There are several types of discrete sphere grid.…”

Section: Introductionmentioning

confidence: 99%

A Barotropic Tide Model for Global Ocean Based on Rotated Spherical Longitude-Latitude Grids

Konečný

Chen

et al. 2021

Water

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Ocean modeling and simulation are important for understanding the dynamic processes in the geophysical system, and the simulation of tidal dynamics is of great significance for understanding the dynamic evolution of the ocean. However, there are some problems in existing simulations, including lack of specific standards to produce a desirable discrete spherical mesh for global ocean modelling. Many global ocean numerical models based on conventional longitude-latitude (LL) coordinates suffer from the “pole problem” in regions adjacent to the North Pole due to the convergence of meridians, which seriously hinders global ocean simulations. In this paper, a new longitude-latitude spherical grid coupled with rotated coordinate mapping is proposed to overcome the problem. In the design of the numerical model, for spatial approximation, the finite volume method on staggered C grid is proposed to solve the two-dimensional tidal wave equations for the global ocean. For temporal integration, the third-order Adams-Bashforth method is used to explicitly extrapolate the value on the next time interval half layer, and then the fourth-order implicit Adams-Moulton method is used to correct the water level. Finally, the constructed model is used to simulate the dynamics of two-dimensional tidal waves in the global ocean, and the co-tidal maps of two major diurnal tide and semidiurnal tide components are shown. The results demonstrate that the proposed model can support the simulation of tidal dynamics in the global ocean, especially for the Arctic Ocean.

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Geographic modeling and simulation systems for geographic research in the new era: Some thoughts on their development and construction

Cited by 29 publications

References 119 publications

A process‐driven geo‐analysis engine to support online collaborative geographic experiments

A process‐driven geo‐analysis engine to support online collaborative geographic experiments

A ConvLSTM Conjunction Model for Groundwater Level Forecasting in a Karst Aquifer Considering Connectivity Characteristics

A Barotropic Tide Model for Global Ocean Based on Rotated Spherical Longitude-Latitude Grids

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