Integration of unsupervised and supervised neural networks to predict dissolved oxygen concentration in canals

Areerachakul, Sirilak; Sophatsathit, Peraphon; Lursinsap, Chidchanok

doi:10.1016/j.ecolmodel.2013.04.002

Cited by 16 publications

(6 citation statements)

References 9 publications

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“…Input data sets were randomly divided into training (80%, 95 sets) and testing factions (20%, 24 sets), with this strategy reiterated 10-fold. 49 Incorporating two hidden layers, the ideal neuron quantity within these layers was determined to be (22, 22) through trial-and-error assessment, culminating in a 6-22-22-1 network configuration. Subsequent to training and testing, the model's predictions were compared with the actual data, as presented in Figure 6, and details are tabulated in Table S1.…”

Section: Resultsmentioning

confidence: 99%

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

Zhang,

Yao,

Zhao

et al. 2024

Ind. Eng. Chem. Res.

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Wastewater treatment, especially the efficient degradation of contaminants such as m-cresol, remains a pivotal challenge. This study investigates the application of artificial neural networks (ANN) in predicting total organic carbon (TOC) removal rates from m-cresol-contaminated wastewater by using the ultraviolet (UV)-Fenton oxidation process. Six key variables, namely, Fe 2+ dosage, H 2 O 2 dosage, catalyst quantity, reaction time, pH, and substrate concentration, were employed as inputs to the ANN model. Leveraging this multivariable input and a comprehensive data set, the ANN model projected a maximum TOC removal rate of 87.12%, validated by an efficiency of 86.26% achieved through experiments under the derived optimal conditions: Fe 2+ dosage at 16.09 mg/L, H 2 O 2 dosage at 1.40 mg/L, catalyst quantity at 0.11 g/L, reaction time of 29.80 min, initial pH of 3.66, and substrate concentration of 50 mg/L. Comparative analysis with other machine learning algorithms further revealed that the ANN model notably outperformed linear regression, support vector regression, and random forest in terms of precision. This work paves the way for resource-optimized experimental designs, fostering real-time wastewater monitoring and refining advanced oxidation process proficiency in industrial applications.

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Section: Resultsmentioning

confidence: 99%

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

Zhang,

Yao,

Zhao

et al. 2024

Ind. Eng. Chem. Res.

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“…The conjugate gradient algorithm was carried out to train the model. Areerachakul et al [144] presented two cluster technique, namely K-means, fuzzy c-means (FCM) in DO prediction. Results indicated that the performance of hybrid methods was better than single models.…”

Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

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Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

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“…To verify the predictive performance of the dissolved oxygen forecasting model based on the ECA-Adam-RBFNN algorithm proposed in this paper, the traditional RBFNN prediction method was selected as well as the LSTM, ARIMA, SVR [48], BPNN [49], [50] , K-MLPNN [51], [52] and SC-K-means-RBF [53] methods to compare their performance in predicting the time series data of the dissolved oxygen in the fishery water ( Fig. 9), (Fig.…”

Section: Ch(k)mentioning

confidence: 99%

Radial Basis Function Neural Network Model for Dissolved Oxygen Concentration Prediction Based on an Enhanced Clustering Algorithm and Adam

Sun

Feng

2021

IEEE Access

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In fishery aquaculture, water quality directly determines the economic benefits of aquatic products, and dissolved oxygen is an important factor affecting water quality. To accurately grasp the trends of variation in dissolved oxygen, a dissolved oxygen concentration forecasting model based on an enhanced clustering algorithm and Adam with a radial basis function neural network (ECA-Adam-RBFNN) is proposed. An enhanced clustering algorithm (ECA) combining K-means with ant colony optimization is introduced in place of random selection to determine the center positions of the neural network hidden layer units. If the number of center points is too high, the neural network will be overfit, whereas if it is too low, sudden changes will appear in the results. Once the hidden layer centers have been determined, the radial basis function (RBF) width is calculated from the maximum center distance and the number of center points to avoid the two extreme cases of RBF that are too peaked or flat. The recursive least squares (RLS) algorithm is introduced to obtain the connection weights from the hidden layer to the output layer. The Adam algorithm is introduced to iteratively differentiate the objective function to adjust the center values, weights and width while adaptively varying the learning rates for these three types of parameters. Finally, the improved forecasting algorithm is applied for the prediction of the dissolved oxygen concentration in fishery aquaculture. The experimental results show that under identical conditions, compared with a long short-term memory (LSTM) network, a backpropagation neural network (BPNN), a traditional RBF neural network, a support vector regression (SVR) model, an autoregressive integrated moving average (ARIMA), K-MLPNN (K-means muhilayer perceptron neural networks), and SC-K-means-RBF model, the improved algorithm achieves significant reductions in the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) as model evaluation indicators.

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Integration of unsupervised and supervised neural networks to predict dissolved oxygen concentration in canals

Cited by 16 publications

References 9 publications

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

A Review of the Artificial Neural Network Models for Water Quality Prediction

Radial Basis Function Neural Network Model for Dissolved Oxygen Concentration Prediction Based on an Enhanced Clustering Algorithm and Adam

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