Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density
| dc.contributor.author | Ekundayo, CT | |
| dc.contributor.author | Oluwatosin, AI. | |
| dc.contributor.author | Igbinosa, EO. | |
| dc.contributor.author | Okoh, AI. | |
| dc.date.accessioned | 2026-03-31T11:44:43Z | |
| dc.date.available | 2026-03-31T11:44:43Z | |
| dc.date.issued | 2022-10-28 | |
| dc.description.abstract | Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (meansquared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R2 =0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection. | |
| dc.identifier.issn | 0269-7491 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11837/3934 | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.subject | Pathogen | |
| dc.subject | Public health | |
| dc.subject | Machine intelligence | |
| dc.subject | Prediction | |
| dc.subject | Feature importance | |
| dc.subject | Predictive microbiology | |
| dc.subject | Multiple linear regression | |
| dc.subject | Random forest | |
| dc.subject | Gradient boosted machine | |
| dc.subject | Neural networks | |
| dc.subject | K-nearest neighbours | |
| dc.subject | Boosted regression tree | |
| dc.subject | Extreme gradient boosted regression | |
| dc.subject | Support vector regression | |
| dc.subject | Decision tree regression | |
| dc.subject | M5 pruned regression | |
| dc.subject | Artificial neural network regression | |
| dc.title | Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density | |
| dc.type | Article | |
| person.identifier.orcid | Ekundayo, CT 0000-0002-7781-3507 | |
| person.identifier.orcid | Oluwatosin, AI. 0000-0002-6283-8517 | |
| person.identifier.orcid | Igbinosa, EO. 0000-0001-7441-2145 | |
| person.identifier.orcid | Okoh, AI. 0000-0002-9770-085X |