Study on the Optimization of Monitoring Well Placement Using Bayesian Model Averaging and Bayesian Maximum Entropy
Notice bibliographique
Résumé
　　Certain limitations arise when utilizing the Monitoring Efficiency Model (MEMO) and Monitoring and Remediation Optimization System (MAROS) to evaluate monitoring well placement at contaminated sites. MEMO is restricted to one-dimensional groundwater flow analysis, while MAROS can only handle two-dimensional spatial distribution of contaminants. These constraints hinder the ability to account for variability in the three-dimensional spatial distribution of contaminants, leading to suboptimal monitoring well configurations. In particular, factors such as geological heterogeneity and contaminant characteristics (e.g., biodegradation, chemical degradation, and physical adsorption) may lead to contaminant omissions or inappropriate monitoring well density distribution, ultimately limiting the efficiency and accuracy of monitoring well placement.　　To address these challenges, this study proposes an optimized approach for monitoring well placement at three-dimensional groundwater contamination sites. The method integrates Bayesian Model Averaging (BMA) and Bayesian Maximum Entropy (BME) to delineate contaminant plumes more accurately and provide optimal recommendations for monitoring well placement. BMA, utilizing Markov Chain Monte Carlo (MCMC) simulations and Bayesian inference, calculates the posterior distribution of multiple potential Conceptual Site Models (CSMs) by evaluating discrepancies between observed and simulated contaminant concentrations.　　Using the weighted CSM, the relative positions between existing monitoring wells and the contaminant plume can be evaluated. During the numerical simulation process, virtual observation points are added to enhance the richness and completeness of data distribution within the contaminated area, further improving the interpolation accuracy of BME. Through this improvement, BME can integrate simulated data with existing monitoring data to precisely predict the locations of additional monitoring wells, supplement critical monitoring data, and optimize the overall monitoring well placement strategy.　　Additionally, this study incorporates monitoring well-installation costs, the value of information (VOI), and trans-information entropy (TE) into a multi-objective optimization framework. By minimizing the objective function, Pareto-optimal solutions are obtained. The Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) is then applied to rank these solutions, enabling decision-makers to balance monitoring efficiency with cost considerations and implement flexible and effective monitoring configurations. It also verifies the feasibility of retaining a significant portion of critical monitoring information through VOI-based quantitative analysis, even with a reduced number of monitoring wells.　　The proposed optimization method has been validated through numerical simulations, demonstrating improved model accuracy under complex site conditions. The results offer adaptable, site-specific solutions that maximize both monitoring efficiency and economic viability. Keywords: Bayesian Model Averaging, Bayesian Maximum Entropy, groundwater contaminant transport, optimization of monitoring well placement
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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 | 0,001 | 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) | 0,000 | 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 ».