Dynamic Bayesian Networks to Assess Anthropogenic and Climatic Drivers of Saltwater Intrusion: A Decision Support Tool Toward Improved Management
Notice bibliographique
Résumé
Abstract Saltwater intrusion (SWI) is a global coastal problem caused by aquifer overpumping, land-use change, and climate change impacts. Given the complex pathways that lead to SWI, coastal urban areas with poorly monitored aquifers are in need of probabilistic-based decision support tools that can assist in better understanding and predicting SWI, while exploring effective means for sustainable aquifer management. In this study, we develop a Bayesian Belief Network (BBN) to account for the complex interactions of climatic and anthropogenic processes leading to SWI, while relating the severity of SWI to associated socioeconomic impacts and possible adaptation strategies. The BBN is further expanded into a Dynamic Bayesian Network (DBN) to assess the temporal progression of SWI and account for the compounding uncertainties over time. The proposed DBN is then tested at a pilot coastal aquifer underlying a highly urbanized water-stressed metropolitan area along the Eastern Mediterranean coastline (Beirut, Lebanon). The results show that the future impacts of climate change are largely secondary when compared to the persistent water deficits. While both supply and demand management could halt the progression of salinity, the potential for reducing or reversing SWI is not evident. The indirect socioeconomic burden associated with aquifer salinity was observed to improve, albeit heterogeneously, with the application of various adaptation strategies; however, this was at a cost associated with the implementation and operation of these strategies. The proposed DBN acts as an effective decision support tool that can promote sustainable aquifer management in coastal regions through its robust representation of the main drivers of SWI and linking them to expected socioeconomic burdens and management options. Integr Environ Assess Manag 2021;17:202–220. © 2020 SETAC KEY POINTS A Dynamic Bayesian Network (DBN) was developed to assess the temporal progression of saltwater intrusion in a coastal aquifer through time-slicing and the inclusion of temporal clones. The DBN consisted of multiple variables categorized into 4 components, namely a climate component, a hydrogeological component, a demographic component, and a socioeconomic component. The DBN results showed that the impacts of climate change were secondary when compared to persistent water deficits in the study area. Both supply and demand management were able to halt the progression of salinity but not reverse it. The developed DBN proved to be an effective decision support tool for promoting sustainable aquifer management in coastal regions through its robust representation of the main drivers of saltwater instrusion (SWI) and linking them to expected socioeconomic burdens and management options.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».