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DC Field | Value | Language |
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dc.citation.startPage | 102370 | - |
dc.citation.title | ECOLOGICAL INFORMATICS | - |
dc.citation.volume | 78 | - |
dc.contributor.author | Jang, Jiyi | - |
dc.contributor.author | Abbas, Ather | - |
dc.contributor.author | Kim, Hyein | - |
dc.contributor.author | Rhee, Chaeyoung | - |
dc.contributor.author | Shin, Seung Gu | - |
dc.contributor.author | Chun, Jong Ahn | - |
dc.contributor.author | Baek, Sangsoo | - |
dc.contributor.author | Cho, Kyung Hwa | - |
dc.date.accessioned | 2024-01-19T12:05:18Z | - |
dc.date.available | 2024-01-19T12:05:18Z | - |
dc.date.created | 2024-01-15 | - |
dc.date.issued | 2023-12 | - |
dc.description.abstract | Recreational beaches face a threat from pathogenic bacteria that harbor antibiotic resistance genes (ARGs). To predict bacterial occurrence and comprehend their non-linear relationship with hydrometeorological features, advanced machine- and deep-learning algorithms were employed. These algorithms include regression trees (RT), as well as interpretable deep-learning algorithms such as the 'Input Attention-Long Short-Term Memory (IA-LSTM)' and 'Temporal Fusion Transformer (TFT)'. Our focus was on predicting the occurrence of Prevotella, a prevalent pathogenic bacterium found at the beaches. Utilizing model-dependent and model-agnostic interpretation methods, which encompass sensitivity analysis, permutation, and the SHapley Additive exPlanations (SHAP) importance, we evaluated model behavior. RT-based algorithms exhibited predictive capabilities comparable to those of IA-LSTM and TFT, achieving validation Nash-Sutcliffe efficiencies of 0.93, 0.94, and 0.96, respectively. However, the deep-learning algorithms (IA-LSTM and TFT) are surpassed in terms of interpretability. The model-dependent interpretation method identified heavy precipitation as a pivotal hydrometeorological feature linked to increased Prevotella occurrence. Notably, the IA-LSTM identified Prevotella as a potential host for the sulfonamide resistance gene (sul1), suggesting the potential of Prevotella as an indicator for sul1. This research, leveraging interpretable data-driven models, advances our understanding of the hydrometeorological features influencing the occurrence of pathogenic bacteria and the prevalence of ARGs at the beach, and enhances predictive capabilities for bacterial occurrence. | - |
dc.identifier.bibliographicCitation | ECOLOGICAL INFORMATICS, v.78, pp.102370 | - |
dc.identifier.doi | 10.1016/j.ecoinf.2023.102370 | - |
dc.identifier.issn | 1574-9541 | - |
dc.identifier.scopusid | 2-s2.0-85178937247 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/68046 | - |
dc.identifier.wosid | 001119368100001 | - |
dc.language | 영어 | - |
dc.publisher | ELSEVIER | - |
dc.title | Prediction and interpretation of pathogenic bacteria occurrence at a recreational beach using data-driven algorithms | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Ecology | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Interpretable models | - |
dc.subject.keywordAuthor | Deep-learning algorithms | - |
dc.subject.keywordAuthor | Strategy modeling | - |
dc.subject.keywordAuthor | Cumulative importance features | - |
dc.subject.keywordAuthor | Simulation | - |
dc.subject.keywordPlus | GLOBAL SENSITIVITY-ANALYSIS | - |
dc.subject.keywordPlus | ESCHERICHIA-COLI | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordPlus | INDEXES | - |
dc.subject.keywordPlus | WATERS | - |
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