The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process

Byliński, Hubert; Sobecki, Andrzej; Gębicki, Jacek

doi:10.3390/su11164407

Cited by 27 publications

(11 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because both real datasets we consider here are not very large (i.e., 303 records in each of them), we can use the K-fold cross-validation technique which is generally very effective with this kind of data. By using the K-fold cross-validation technique, the problem of data bias can be minimized [44]. As mentioned previously, our base nu-SVC algorithm was used with four different kernel functions (linear, polynomial, RBF and sigmoid).…”

Section: Algorithm 1 a General Nested Ensemble (Ne) Modelmentioning

confidence: 99%

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

et al. 2019

View full text Add to dashboard Cite

Coronary artery disease (CAD) is one of the main causes of cardiac death around the world. Due to its significant impact on the society, early and accurate detection of CAD is essential. This study proposes a novel nested ensemble nu-Support Vector Classification (NE-nu-SVC) model which combines several traditional machine learning methods and ensemble learning techniques for effective diagnosis of CAD. We validated our model using two well-known CAD datasets (Z-Alizadeh Sani and Cleveland). To improve the performance of the model, we selected clinically significant features from the datasets using a genetic search algorithm. To further improve our results, we applied a multi-level filtering technique to balance the data using the ClassBlancer and Resample methods. Our base algorithm, nu-SVC, is performed using four well-known kernel functions (linear, polynomial, radial basis (RBF) and sigmoid). The proposed NE-nu-SVC model provided the highest accuracy of 94.66% and 98.60% to predict CAD entities in the Z-Alizadeh Sani and Cleveland CAD datasets, respectively. Our system can aid the clinicians to diagnose CAD accurately and may probably replace other invasive diagnostic techniques. INDEX TERMS Coronary artery disease (CAD), machine learning, ensemble learning, nested ensemble (NE) model, genetic algorithm.

show abstract

Section: Algorithm 1 a General Nested Ensemble (Ne) Modelmentioning

confidence: 99%

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The ANN for the prediction of odor properties of post-fermentation sludge from a biological-mechanical wastewater treatment plant was developed. The ANN comprised of four layers: input layer (eight neurons), two hidden layers (four and two neurons) and output layer (one neuron) [51]. In medicine, ANNs are used to predict mortality risk and incidence of disease.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Francik

Kurpaska

2020

Sensors

View full text Add to dashboard Cite

It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

show abstract

“…They concluded that AI-based models are reliable and full-scale in forecasting horizons. Bylinski et al [56] concluded that using an artificial neural network (ANN) grants a great reflection of complex dependencies of the wastewater management problems, without considering detailed mechanisms of specified processes.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis

2020

View full text Add to dashboard Cite

Wastewater contains considerable amounts of thermal energy. Heat recovery from wastewater in buildings could supply cities with an additional source of renewable energy. However, variations in wastewater temperature influence the performance of the wastewater treatment plant. Thus, the treatment is negatively affected by heat recovery upstream of the plant. Therefore, it is necessary to develop more accurate models of the wastewater temperature variations. In this work, a computational model based on artificial neural network (ANN) is proposed to calculate wastewater treatment plant influent temperature concerning ambient temperature, building effluent temperature and flowrate, stormwater flowrate, infiltration flowrate, the hour of day, and the day of year. Historical data related to the Stockholm wastewater system are implemented in MATLAB software to drive the model. The comparison of calculated and observed data indicated a negligible error. The main advantage of this ANN model is that it only uses historical data commonly recorded, without any requirements of field measurements for intricate heat transfer models. Moreover, Monte Carlo sensitivity analysis determined the most influential parameters during different seasons of the year. Finally, it was shown that installing heat exchangers in 40% of buildings would reduce 203 GWh year−1 heat loss in the sewage network. However, heat demand in WWTP would be increased by 0.71 GWh year−1, and the district heating company would recover 176 GWh year−1 less heat from treated water.

show abstract

The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process

Cited by 27 publications

References 35 publications

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis

Contact Info

Product

Resources

About