Simplifying the deployment of OGC web processing services (WPS) for environmental modelling – Introducing Tethys WPS Server

Qiao, Xiaohui; Li, Zhiyu; Ames, Daniel P.; Nelson, Everett J.; Swain, Nathan

doi:10.1016/j.envsoft.2019.01.021

Cited by 20 publications

(7 citation statements)

References 27 publications

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“…Django covers many of the web framework best practices such as, Internationalization, Forms Validations, AJAX support, and Object Relational Mapping, HTML5 Support, between many others (Salas-Zárate et al, 2015), which makes it a robust tool for web development. That's why it is broadly used in geospatial data management applications (Baranovskiy & Zharikova, 2014;Evans et al, 2020;Qiao et al, 2019;Tarboton et al, 2014).…”

Section: Web Framework Implementationmentioning

confidence: 99%

The Open Database of Regional Models of the International Service for the Geoid

Toro

Carrión

Rossi

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper describes the development of a web application to facilitate the management and processing of geoid models in the ISG collection as well as of additional information available in the ISG website (mainly projects, services and publications). The ISG service pays significant attention to metadata and data interoperability from published geoid models. As a result, the possibility of creating a database with all necessary information for describing geoid models has been considered. PostgreSQL was chosen as the database for the implementation to take advantage of PostGIS geographic processing functions for developing a Database Management System for the resources available inside the ISG service catalogue (including geoid products, software, publications, etc.). The project's web development environment is Django, a web framework which is supported by the PostgreSQL database back-end and which makes it easier to integrate new applications or processing services into the website. A model solution for raster data integration using a PostGIS database back-end named Django-raster has also been investigated as part of the implementation, provided also that it is possible to take advantage of the vast Django community. The Django-raster model allows for the storage of datasets as tiles in the database, their exposure as a Tiled Map Services (TMS), and allows as well raster map algebra operations. TMS services will be utilized to share the geoid models and metadata included in the open access archive using a WebGIS, created with OpenLayers.

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Section: Web Framework Implementationmentioning

confidence: 99%

The Open Database of Regional Models of the International Service for the Geoid

Toro

Carrión

Rossi

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…We implemented all the methods reported here in an open-source, web-based application called the Groundwater Level Mapping Tool (GLMT) [40][41][42]. The interface for the GLMT is shown in Figure 3.…”

Section: Overview and Data Sourcesmentioning

confidence: 99%

“…GLMT performs this data imputation algorithm for each well in the aquifer, then uses the time series generated at each well location to compute water level maps through time. We implemented the computational routines using the Python programing language with extensive use of the NumPy, SciPy, Hydrostats, and HydroErr python libraries [43][44][45][46], while the web interface was developed using Tethys Platform, a library for developing environmental web applications [40,47].…”

Section: Overview and Data Sourcesmentioning

confidence: 99%

Exploiting Earth Observation Data to Impute Groundwater Level Measurements with an Extreme Learning Machine

et al. 2020

Self Cite

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Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture models, the Global Land Data Assimilation System (GLDAS) model and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) soil moisture model to impute the missing data. Our imputation method uses a machine learning technique called Extreme Learning Machine (ELM). Our implementation uses 11 input data-streams, all based on Earth observation data. We train and apply the model one well at a time. We selected ELM because it is a single hidden layer feedforward model that can be trained quickly on minimal data. We tested the ELM method using data from monitoring wells in the Cedar Valley and Beryl-Enterprise areas in southwest Utah, USA. We compute error estimates for the imputed data and show that ELM-computed estimates were more accurate than Kriging estimates. This ELM-based data imputation method can be used to impute missing data at wells. These complete time series can be used improve the accuracy of aquifer groundwater elevation maps in areas where in-situ well measurements are sparse, resulting in more accurate spatial estimates of the groundwater surface. The data we use are available globally from 1950 to the present, so this method can be used anywhere in the world.

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“…It is important to note here that in support to these more integrative explorations, the hydroinformatics community has been developing and demonstrating: (a) Integrated modelling frameworks [73,74]; (b) models as services, often based on open source solutions [75]; and (c) cloud-based modelling systems [76,77], sometimes coupling both local model components and remote web services [78] in an effort to reduce the overhead required to create an integrated model in the first place and make their explorative power more accessible to the water research and practitioner communities.…”

Section: New Whole Water Cycle Socio-technical System Modelsmentioning

confidence: 99%

Urban Hydroinformatics: Past, Present and Future

Makropoulos

Savić

2019

Water

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Hydroinformatics, as an interdisciplinary domain that blurs boundaries between water science, data science and computer science, is constantly evolving and reinventing itself. At the heart of this evolution, lies a continuous process of critical (self) appraisal of the discipline’s past, present and potential for further evolution, that creates a positive feedback loop between legacy, reality and aspirations. The power of this process is attested by the successful story of hydroinformatics thus far, which has arguably been able to mobilize wide ranging research and development and get the water sector more in tune with the digital revolution of the past 30 years. In this context, this paper attempts to trace the evolution of the discipline, from its computational hydraulics origins to its present focus on the complete socio-technical system, by providing at the same time, a functional framework to improve the understanding and highlight the links between different strands of the state-of-art hydroinformatic research and innovation. Building on this state-of-art landscape, the paper then attempts to provide an overview of key developments that are coming up, on the discipline’s horizon, focusing on developments relevant to urban water management, while at the same time, highlighting important legal, ethical and technical challenges that need to be addressed to ensure that the brightest aspects of this potential future are realized. Despite obvious limitations imposed by a single paper’s ability to report on such a diverse and dynamic field, it is hoped that this work contributes to a better understanding of both the current state of hydroinformatics and to a shared vision on the most exciting prospects for the future evolution of the discipline and the water sector it serves.

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Simplifying the deployment of OGC web processing services (WPS) for environmental modelling – Introducing Tethys WPS Server

Cited by 20 publications

References 27 publications

The Open Database of Regional Models of the International Service for the Geoid

The Open Database of Regional Models of the International Service for the Geoid

Exploiting Earth Observation Data to Impute Groundwater Level Measurements with an Extreme Learning Machine

Urban Hydroinformatics: Past, Present and Future

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