Developing Blue-Green Infrastructure: Advantages and Challenges Through Natural Capital, Ecosystem Services, and Machine Learning
Notice bibliographique
Résumé
Addressing techniques on water resources towards sustainability and resilient cities relies on mechanisms that create conditions to foster the initial natural conditions and reduce the gap between human development and environmental necessity. Blue-green infrastructure (BGI) has emerged as a transformative solution for water adaptation, offering ecological, social, and economic benefits over gray infrastructure. Inspirated by natural processes, BGI not only restores environmental equilibrium but also enhances its ecosystem services, such as flood mitigation, water quality improvement, and urban cooling. Adopting blue-green infrastructure not only restores the natural conditions for water resources but also allows the recovery of the natural capital assets – the stock of natural resources – and its ecosystem services. These natural capital’s assets provide ecosystem services that benefit humans and their wellbeing, such as cleaning water, climate regulation, carbon sequestration, pollination, and water availability. However, the comprehension of geospatial indicators and conditions that influence the ecosystem services is a handful knowledge that leads to the implementation of blue-green technologies for water resources management. In a global warming context, characterized by more frequent and severe extreme events, such as floods and droughts, more adaptative and resilient infrastructure for water resources management is required, allowing an interconnected solution that encompass several parts of the society pursuing sustainability and benefit to human and environment. Meanwhile, the advancement in machine learning is also a promising mechanism that can be applied to water resources to handle prominent problems, offering improved decision support systems and often outperforming traditional models. Furthermore, the machine learning algorithms have been successfully used for integrated management of river-reservoir systems and real-time control of sewer systems. Hence, this research aims to develop a machine learning model to assess the impact of various spatial indicators on water ecosystem services. Initially, a random forest analysis is being undertaken to measure the correlation between several spatial indicators (or drivers) and ecosystem services. Some of the spatial indicators are land use, biome, precipitation, evapotranspiration, urbanization, etc. The ecosystem services evaluated in this study are based on the Nature’s Contributions to People (NCP) 6 (water quantity and flow regulation) and 9 (hazard regulation), from Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). This methodology will be applied to several continental basins worldwide, encompassing diverse conditions. Finally, this approach aims to quantify the influence of key drivers on water resources and guide decision-makers in adopting blue-green infrastructure. By doing so, it seeks to enhance ecosystem services, benefiting both society and the environment.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,004 | 0,007 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».