A data model to manage data for water resources systems modeling

Abdallah, Adel M.; Rosenberg, David E.

doi:10.1016/j.envsoft.2019.02.005

Cited by 11 publications

(4 citation statements)

References 45 publications

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“…ODM has also been extended and generalized in [34] to deal with discrete Earth observations. In [35], the authors review more than 40 systems for water management and propose a new water management data model (WaMDaM).…”

Section: Geospatial and Environmental Data Modeling And Storagementioning

confidence: 99%

“…ISO 19115 provides also means for the modeling of geospatial data quality. Available observation data models [11,31,33,35,56] provide constructs for the representation of observation processes and features of interest, including sampling features. Complex coverage results and data quality metadata are also supported.…”

Section: Challenge 3: Environmental Observation and Modeling Ontologymentioning

confidence: 99%

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Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

et al. 2020

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The monitoring and forecasting of environmental conditions is a task to which much effort and resources are devoted by the scientific community and relevant authorities. Representative examples arise in meteorology, oceanography, and environmental engineering. As a consequence, high volumes of data are generated, which include data generated by earth observation systems and different kinds of models. Specific data models, formats, vocabularies and data access infrastructures have been developed and are currently being used by the scientific community. Due to this, discovering, accessing and analyzing environmental datasets requires very specific skills, which is an important barrier for their reuse in many other application domains. This paper reviews earth science data representation and access standards and technologies, and identifies the main challenges to overcome in order to enable their integration in semantic open data infrastructures. This would allow non-scientific information technology practitioners to devise new end-user solutions for citizen problems in new application domains.

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Section: Geospatial and Environmental Data Modeling And Storagementioning

confidence: 99%

Section: Challenge 3: Environmental Observation and Modeling Ontologymentioning

confidence: 99%

Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

et al. 2020

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“…Further, the database architecture even from the state to municipal level is different, so data cannot be seamlessly integrated. Data often need to be translated or transformed when transferred between organizations, requiring a significant time investment in the investigator's analysis (Abdallah and Rosenberg 2019). The lack of continuity between systems limits the efficiency and insight that stormwater infrastructure data can provide.…”

Section: Introductionmentioning

confidence: 99%

A Relational Data Model for Advancing Stormwater Infrastructure Management

Smith

McGauley

Newman

et al. 2023

J. Sustainable Water Built Environ.

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To date, the knowledge benefits that can result from the growing abundance of measured stormwater data have yet to be fully realized within the industry. Obstacles due to data format and storage, retrieval, and quality control have limited the size and impact of accessing known data, resulting in data that are often siloed by project or objective, which inhibits advancements in our understanding, as well as decision making and information sharing across municipalities. Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI)'s observations data model (ODM) is a relational data model designed to organize disparate types of data; however, it does not provide avenues for the storage of stormwater monitoring-specific metadata. To facilitate stormwater infrastructure data analysis, the Villanova stormwater infrastructure data model (SIDM), constructed comparably to ODM1, was created by adding and removing tables from the ODM1 structure, along with other modifications to enable efficient data access. The novel data model presented here facilitates a comprehensive spatiotemporal analysis specific to stormwater systems to overcome traditional stormwater data siloes and harness the benefits of stormwater data through efficient avenues. The data model structure of the SIDM enables stormwater-specific data to be managed efficiently not only to provide data regarding function and performance but to advance the science around stormwater planning and management, as well as knowledge sharing across researchers, utilities, and municipalities. In this paper, we describe the logic of the design, benefits of the data model, and applications to specific green stormwater infrastructure installations. This has the potential to not only provide novel insight but open the door for applications of modeling advancement in physics-based models and beyond to artificial intelligence, data analysis, and data-informed decision making in the planning, design, operation, and maintenance of stormwater systems.

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“…Additionally, the process structure may be modified to adapt to the feed properties. Thus, the model usually becomes increasingly ineffective, because its generalization ability is limited in data-based modeling [4]. If the model cannot accurately reflect the behavior of the process, the model-mismatch problem occurs.…”

Section: Introductionmentioning

confidence: 99%

ELM-Based AFL–SLFN Modeling and Multiscale Model-Modification Strategy for Online Prediction

et al. 2019

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Online prediction of key parameters (e.g., process indices) is essential in many industrial processes because online measurement is not available. Data-based modeling is widely used for parameter prediction. However, model mismatch usually occurs owing to the variation of the feed properties, which changes the process dynamics. The current neural network online prediction models usually use fixed activation functions, and it is not easy to perform dynamic modification. Therefore, a few methods are proposed here. Firstly, an extreme learning machine (ELM)-based single-layer feedforward neural network with activation-function learning (AFL-SLFN) is proposed. The activation functions of the ELM are adjusted to enhance the ELM network structure and accuracy. Then, a hybrid model with adaptive weights is established by using the AFL-SLFN as a sub-model, which improves the prediction accuracy. To track the process dynamics and maintain the generalization ability of the model, a multiscale model-modification strategy is proposed. Here, small-, medium-, and large-scale modification is performed in accordance with the degree and the causes of the decrease in model accuracy. In the small-scale modification, an improved just-in-time local modeling method is used to update the parameters of the hybrid model. In the medium-scale modification, an improved elementary effect (EE)-based Morris pruning method is proposed for optimizing the sub-model structure. Remodeling is adopted in the large-scale modification. Finally, a simulation using industrial process data for tailings grade prediction in a flotation process reveals that the proposed method has better performance than some state-of-the-art methods. The proposed method can achieve rapid online training and allows optimization of the model parameters and structure for improving the model accuracy.Processes 2019, 7, 893 2 of 23 Two kinds of methods are typically used to solve model-mismatch problems [5,6]. The first involves retraining the model using the most recent samples. For example, Feng et al. [6] used the object-reidentification method to deal with object changes. The second kind involves adjusting the structure and parameters of the model according to the original model [7][8][9]. Reidentification of the model parameters cannot always maintain good model performance, because the model structure is important. Thus, updating the model at different scales is necessary. For online updating, the computation time of the algorithm is also significant.As for retraining and updating the model parameters, in a common method, the most recent samples are used to re-estimate the model parameters offline or online. For example, Feng et al. [6] used an object-reidentification method to deal with changes in the object model and object-mismatch problems. Wang et al.[10] used a four-step recursive algorithm to estimate the weights of a neural network. Here, online spatiotemporal measurements were performed to obtain time-varying model parameters.In recent years, for selecting relate...

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A data model to manage data for water resources systems modeling

Cited by 11 publications

References 45 publications

Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

A Relational Data Model for Advancing Stormwater Infrastructure Management

ELM-Based AFL–SLFN Modeling and Multiscale Model-Modification Strategy for Online Prediction

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