Comparing Regression Models with Count Data to Artificial Neural Network and Ensemble Models for Prediction of Generic Escherichia coli Population in Agricultural Ponds Based on Weather Station Measurements

Buyrukoğlu, Gonca; Buyrukoğlu, Selim; Topalcengiz, Zeynal

doi:10.1016/j.mran.2021.100171

Cited by 24 publications

(14 citation statements)

References 48 publications

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“…The findings from this study are consistent with past studies focused on predicting enteric pathogen presence in agricultural water Polat et al (2019), Weller et al (2020c), fecal indicator levels in agricultural water Buyrukoglu et al (2021), Weller et al 2). The x-axis of each plot is normalized variable importance (VI), and the features on the y-axis are arranged from greatest impact (higher VI) to lowest impact (lower VI) on predictive accuracy.…”

Section: Discussionsupporting

confidence: 92%

Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water

Weller

Love

2021

Front. Environ. Sci.

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Recent studies have shown that predictive models can supplement or provide alternatives to E. coli-testing for assessing the potential presence of food safety hazards in water used for produce production. However, these studies used balanced training data and focused on enteric pathogens. As such, research is needed to determine 1) if predictive models can be used to assess Listeria contamination of agricultural water, and 2) how resampling (to deal with imbalanced data) affects performance of these models. To address these knowledge gaps, this study developed models that predict nonpathogenic Listeria spp. (excluding L. monocytogenes) and L. monocytogenes presence in agricultural water using various combinations of learner (e.g., random forest, regression), feature type, and resampling method (none, oversampling, SMOTE). Four feature types were used in model training: microbial, physicochemical, spatial, and weather. “Full models” were trained using all four feature types, while “nested models” used between one and three types. In total, 45 full (15 learners*3 resampling approaches) and 108 nested (5 learners*9 feature sets*3 resampling approaches) models were trained per outcome. Model performance was compared against baseline models where E. coli concentration was the sole predictor. Overall, the machine learning models outperformed the baseline E. coli models, with random forests outperforming models built using other learners (e.g., rule-based learners). Resampling produced more accurate models than not resampling, with SMOTE models outperforming, on average, oversampling models. Regardless of resampling method, spatial and physicochemical water quality features drove accurate predictions for the nonpathogenic Listeria spp. and L. monocytogenes models, respectively. Overall, these findings 1) illustrate the need for alternatives to existing E. coli-based monitoring programs for assessing agricultural water for the presence of potential food safety hazards, and 2) suggest that predictive models may be one such alternative. Moreover, these findings provide a conceptual framework for how such models can be developed in the future with the ultimate aim of developing models that can be integrated into on-farm risk management programs. For example, future studies should consider using random forest learners, SMOTE resampling, and spatial features to develop models to predict the presence of foodborne pathogens, such as L. monocytogenes, in agricultural water when the training data is imbalanced.

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Section: Discussionsupporting

confidence: 92%

Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water

Weller

Love

2021

Front. Environ. Sci.

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“…We compared only five different algorithms in this study which were chosen due their popularity, but many more ML algorithms and their modifications exist and can be tested for regression-type application (Kuhn, 2008 ; Kuhn and Johnson, 2013 ; Weller et al, 2021 ). We chose not to run artificial neural networks (ANN) due to constraints of the dataset dimensions but other researchers have found success in applying ANN algorithms in the field of microbial water quality (Motamarri and Boccelli, 2012 ; Buyrukoglu et al, 2021 ). Other promising algorithms for water quality determinations include those founded in Bayesian statistical methods such as Naïve Bayes or Bayesian Belief Networks (Avila et al, 2018 ; Panidhapu et al, 2020 ) and the use of ensemble or model stacking methods (Buyrukoglu et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…We chose not to run artificial neural networks (ANN) due to constraints of the dataset dimensions but other researchers have found success in applying ANN algorithms in the field of microbial water quality (Motamarri and Boccelli, 2012 ; Buyrukoglu et al, 2021 ). Other promising algorithms for water quality determinations include those founded in Bayesian statistical methods such as Naïve Bayes or Bayesian Belief Networks (Avila et al, 2018 ; Panidhapu et al, 2020 ) and the use of ensemble or model stacking methods (Buyrukoglu et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Prediction of E. coli Concentrations in Agricultural Pond Waters: Application and Comparison of Machine Learning Algorithms

Stocker

Pachepsky

Hill

2022

Front. Artif. Intell.

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The microbial quality of irrigation water is an important issue as the use of contaminated waters has been linked to several foodborne outbreaks. To expedite microbial water quality determinations, many researchers estimate concentrations of the microbial contamination indicator Escherichia coli (E. coli) from the concentrations of physiochemical water quality parameters. However, these relationships are often non-linear and exhibit changes above or below certain threshold values. Machine learning (ML) algorithms have been shown to make accurate predictions in datasets with complex relationships. The purpose of this work was to evaluate several ML models for the prediction of E. coli in agricultural pond waters. Two ponds in Maryland were monitored from 2016 to 2018 during the irrigation season. E. coli concentrations along with 12 other water quality parameters were measured in water samples. The resulting datasets were used to predict E. coli using stochastic gradient boosting (SGB) machines, random forest (RF), support vector machines (SVM), and k-nearest neighbor (kNN) algorithms. The RF model provided the lowest RMSE value for predicted E. coli concentrations in both ponds in individual years and over consecutive years in almost all cases. For individual years, the RMSE of the predicted E. coli concentrations (log10 CFU 100 ml−1) ranged from 0.244 to 0.346 and 0.304 to 0.418 for Pond 1 and 2, respectively. For the 3-year datasets, these values were 0.334 and 0.381 for Pond 1 and 2, respectively. In most cases there was no significant difference (P > 0.05) between the RMSE of RF and other ML models when these RMSE were treated as statistics derived from 10-fold cross-validation performed with five repeats. Important E. coli predictors were turbidity, dissolved organic matter content, specific conductance, chlorophyll concentration, and temperature. Model predictive performance did not significantly differ when 5 predictors were used vs. 8 or 12, indicating that more tedious and costly measurements provide no substantial improvement in the predictive accuracy of the evaluated algorithms.

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“…For the CAIMTUSNet, a study to be carried out on ensemble models, which has become increasingly widespread in recent years, is planned as future work. Ensemble models often achieve successful results in studies such as segmentation [51,52] with DL, detection [53], and feature selection [54,55] with ML algorithms. For this reason, ensemble feature selection and ensemble model comparison studies can be performed in the CAIMTUSNet.…”

Section: Discussionmentioning

confidence: 99%

Karotis Arter Intima-Medya Kalınlığı Ultrason Görüntülerinde Derin Öğrenme Modellerinin Karşılaştırılması: CAIMTUSNet

Topaloğlu

Kazci

Koşar

2022

Bilişim Teknolojileri Dergisi

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Deep learning is a machine learning technique that uses deep neural networks, which are multilayer neural networks that contain two or more hidden layers. In recent years, deep learning algorithms are also used to solve machine learning problems in medicine. Carotid artery disease is a type of cardiovascular disease that can result in a stroke. If a stroke is not diagnosed early, it is in the first place among the disabling diseases and the third place for the most common cause of death after cancer and heart disease. In this study, the classification performances of deep learning architectures in the biomedical field are compared, and Carotid Artery (CA) Intima-Media Thickness (IMT) Ultrasound (US) images were used. For an early diagnosis, AlexNet, ZFNet, VGGNet (16-19), which had successful results in the ImageNet competition, and authors' original CNNcc models were used for comparison. An image database of CA-IMT-US which contains 501 ultrasound images from 153 patients was used to test the models' classification performances. It is seen that AlexNet, ZFNet, VGG16, VGG19, and CNNcc models achieved rates of 91%, 89.1%, 93%, 90%, and 89.1% respectively. The CNNcc model was found to produce successful classification results on CAIMTUS images when different performance indicators are also taken into account. In addition, different performance indicators including confusion matrices were investigated and the results were announced. The results showed that deep architectures are promising in the biomedical field and can provide proper classification on biomedical images so; this can help clinics to diagnose the disease early.

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Comparing Regression Models with Count Data to Artificial Neural Network and Ensemble Models for Prediction of Generic Escherichia coli Population in Agricultural Ponds Based on Weather Station Measurements

Cited by 24 publications

References 48 publications

Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water

Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water

Prediction of E. coli Concentrations in Agricultural Pond Waters: Application and Comparison of Machine Learning Algorithms

Karotis Arter Intima-Medya Kalınlığı Ultrason Görüntülerinde Derin Öğrenme Modellerinin Karşılaştırılması: CAIMTUSNet

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