Chisha Chongo Mzyece, Miriam Glendell, Zisis Gagkas, Mads Troldborg, Camilla Negri, Eulyn Pagaling, Ian Jones, David M. Oliver
{"title":"Validating a Bayesian network model to characterise faecal indicator organism loss from septic tank systems in rural catchments","authors":"Chisha Chongo Mzyece, Miriam Glendell, Zisis Gagkas, Mads Troldborg, Camilla Negri, Eulyn Pagaling, Ian Jones, David M. Oliver","doi":"10.1016/j.watres.2025.124715","DOIUrl":null,"url":null,"abstract":"Validating model predictions with observed data is crucial for fostering confidence in model results, yet it is often overlooked in Bayesian Network (BN) studies. This research validated a BN model designed to predict faecal indicator organism (FIO) loss from septic tank systems (STS) in rural catchments (Cessnock and Mein). Both a hybrid model (combining continuous and discrete variables) and a fully discretised model were assessed. Our approach to model validation employed four methods: (1) comparing probability distributions of simulated and observed FIO loads in the hybrid model, (2) sensitivity analysis in the discrete model to identify key variables influencing results, (3) estimating percentage bias to evaluate the average difference between predicted and observed FIO loads in the hybrid model, and (4) applying Shannon entropy to measure uncertainty in the discrete model’s spatial application. Predicted FIO loads per STS were consistent across models, with the hybrid network estimating 4.63 × 10¹⁰ cfu/yr in the Cessnock catchment and 4.36 × 10¹⁰ cfu/yr in the Mein catchment, while the discrete network predicted 3.85 × 10¹⁰ cfu/yr and 3.65 × 10¹⁰ cfu/yr, respectively, closely aligning with observed values of 6.17 × 10¹⁰ cfu/yr and 5.10 × 10¹⁰ cfu/yr. Sensitivity analysis identified STS condition and treatment level as critical factors influencing FIO loss. Shannon entropy values (1.60–1.85) revealed significant uncertainty in model predictions in the catchment where STS were associated with a variability of Hydrology of Soil Types (HOST)-derived risk factors. When applied at national scale, greater confidence in model results was associated with Central, East and West Scotland where most STS were associated with a moderate to high HOST-derived risk classification. Our research is the first to show how BN models can predict FIO pollution from STS to watercourses and the findings suggest that refining model predictions requires more accurate data on STS treatment levels and maintenance, as well as access to good quality high-resolution stream water quality monitoring data.","PeriodicalId":443,"journal":{"name":"Water Research","volume":"4 1","pages":""},"PeriodicalIF":12.4000,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.watres.2025.124715","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Validating model predictions with observed data is crucial for fostering confidence in model results, yet it is often overlooked in Bayesian Network (BN) studies. This research validated a BN model designed to predict faecal indicator organism (FIO) loss from septic tank systems (STS) in rural catchments (Cessnock and Mein). Both a hybrid model (combining continuous and discrete variables) and a fully discretised model were assessed. Our approach to model validation employed four methods: (1) comparing probability distributions of simulated and observed FIO loads in the hybrid model, (2) sensitivity analysis in the discrete model to identify key variables influencing results, (3) estimating percentage bias to evaluate the average difference between predicted and observed FIO loads in the hybrid model, and (4) applying Shannon entropy to measure uncertainty in the discrete model’s spatial application. Predicted FIO loads per STS were consistent across models, with the hybrid network estimating 4.63 × 10¹⁰ cfu/yr in the Cessnock catchment and 4.36 × 10¹⁰ cfu/yr in the Mein catchment, while the discrete network predicted 3.85 × 10¹⁰ cfu/yr and 3.65 × 10¹⁰ cfu/yr, respectively, closely aligning with observed values of 6.17 × 10¹⁰ cfu/yr and 5.10 × 10¹⁰ cfu/yr. Sensitivity analysis identified STS condition and treatment level as critical factors influencing FIO loss. Shannon entropy values (1.60–1.85) revealed significant uncertainty in model predictions in the catchment where STS were associated with a variability of Hydrology of Soil Types (HOST)-derived risk factors. When applied at national scale, greater confidence in model results was associated with Central, East and West Scotland where most STS were associated with a moderate to high HOST-derived risk classification. Our research is the first to show how BN models can predict FIO pollution from STS to watercourses and the findings suggest that refining model predictions requires more accurate data on STS treatment levels and maintenance, as well as access to good quality high-resolution stream water quality monitoring data.
期刊介绍:
Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include:
•Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management;
•Urban hydrology including sewer systems, stormwater management, and green infrastructure;
•Drinking water treatment and distribution;
•Potable and non-potable water reuse;
•Sanitation, public health, and risk assessment;
•Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions;
•Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment;
•Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution;
•Environmental restoration, linked to surface water, groundwater and groundwater remediation;
•Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts;
•Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle;
•Socio-economic, policy, and regulations studies.