Near real-time water quantity monitoring data assets collected, managed, analyzed and disseminated by the MNRF and MOECC
Notice bibliographique
Résumé
Based within the Ontario Ministry of Natural Resources and Forestry (MNRF), the Surface Water Monitoring Centre (SWMC) is the primary Ontario government office tasked with Emergency Management responsibilities for flood forecasting and drought monitoring. To fulfil these mandates, the SWMC polls and ingests water monitoring data in near real-time, from approximately 2000 monitoring sites from 11 different sensor networks spanning the province, and adjacent jurisdictions. Central to the SWMC's operational near real-time data stream are 600 strategically located hydrometric stream gauge sites that are cost-shared with Environment Canada -Water Survey of Canada (EC-WSC). The SWMC ingests data from an additional 1400 monitoring stations through an array of partnerships with agencies such as the Meteorological Service of Canada (MSC), Parks Canada (PC), all 36 Conservation Authorities (CA), the International Joint Commission (IJC), NOAA, Ontario Power Generation (OPG), the Ontario Ministry of Transportation (MTO) and a Citizen Weather Science initiative called CoCoRaHS. Along with hydrometric level and flow data, the SWMC captures, archives and disseminates precipitation, wind, temperature (water and air), air pressure, wave buoy, soil moisture, snow-depth and snow water equivalent data from across Ontario. Beginning in 2014 the SWMC, in cooperation with the MOECC began ingesting groundwater level data from 100 MOECC Provincial Groundwater Monitoring Network (PGMN) wells in near real-time via GOES satellite telemetry. As well beginning in 2015, the MOECC has initiated the migration of the Provincial Groundwater Monitoring Information System (PGMIS) period-of-record into what is becoming a shared MNRF/MOECC water quantity monitoring data environment. To accommodate this large and ever increasing near real-time data stream and the entire period-of-record associated with each gauge/site (in some cases >100 years), this shared data environment requires robust IT infrastructure and software. Further, because the SWMC provides critical Emergency Management services, we are obligated to operate 24/7 in a restrictive and secure IT environment with numerous hardware and software redundancies. In common with the majority of Ontario's 36 Conservation Authorities (our central local flood forecasting, drought monitoring and groundwater monitoring partners in Ontario) our primary water monitoring data management software solution is a KISTERS product, WISKI (Water Information Systems KISTERS). This presentation will highlight the MNRF/MOECC's near real-time water quantity monitoring data assets, data flows, polling and telemetry systems/ability, core IT KISTERS data management infrastructure, spatial/temporal (ESRI/Kisters) integration, integrated data linkages to operational models/reports/maps, automated alarm systems, the various sensor networks utilized, the network partnerships that have developed , current web-based near-real-time data reporting services (WISKI WebPro) and the array of flood forecasting and drought monitoring products and services provided to Conservation Authorities and Ontario based water resource partner agencies. Lastly we are currently halfway through a major KISTERS version upgrade (WISKI 7). With this latest version we will have the ability to both consume and publish real-time hydrological data over the Internet using OGC (Open Geospatial Consortium) standards like WaterML2.0. This development, consistent with the Ontario Government's commitment to Open Data, will allow us to expose our entire data stream and period-of-record archive globally via a simple web browser.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».