A Hybrid Prediction Framework for Water Quality with Integrated W-ARIMA-GRU and LightGBM Methods

Zhou, Shenghan; Song, Chaofei; Zhang, Jingjing; Chang, Wenbing; Hou, Wenkui; Yang, Linchao

doi:10.3390/w14091322

Cited by 13 publications

(7 citation statements)

References 36 publications

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“…Zhou et al used the mixture of integrated wavelet decomposition, autoregressive integrated moving average (ARIMA), and a managed recurrent unit (GRU) model, abbreviated as the W-ARIMA-GRU model, to predict the water quality. The results were satisfactory, with an accuracy of more than 97% [12].…”

Section: Introductionmentioning

confidence: 75%

Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches

Pierre,

Akim,

Semenyo

et al. 2023

Energies

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Forecasting peak electrical energy consumption is important because it allows utilities to properly plan for the production and distribution of electrical energy. This reduces operating costs and avoids power outages. In addition, it can help reduce environmental impact by allowing for more efficient power generation and reducing the need for additional fossil fuels during periods of high demand. In the current work, electric power consumption data from “Compagnie Electrique du Benin (CEB)” was used to deduce the peak electric power consumption at peak hours. The peak consumption of electric power was predicted using hybrid approaches based on traditional time series prediction methods (autoregressive integrated moving average (ARIMA)) and deep learning methods (long short-term memory (LSTM), gated recurrent unit (GRU)). The ARIMA approach was used to model the trend term, while deep learning approaches were employed to interpret the fluctuation term, and the outputs from these models were combined to provide the final result. The hybrid approach, ARIMA-LSTM, provided the best prediction performance with root mean square error (RMSE) of 7.35, while for the ARIMA-GRU hybrid approach, the RMSE was 9.60. Overall, the hybrid approaches outperformed the single approaches, such as GRU, LSTM, and ARIMA, which exhibited RMSE values of 18.11, 18.74, and 49.90, respectively.

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Section: Introductionmentioning

confidence: 75%

Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches

Pierre,

Akim,

Semenyo

et al. 2023

Energies

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“…The isolated forest algorithm was employed in this study to detect outliers. The isolated forest (iForest) algorithm is an integrated learning-based unsupervised anomaly detection technique that doesn't require prior knowledge of the training set's label information [21]. The iForest divides the data space, which contains all of the samples, into two subspaces along a given dimension using a random hyperplane.…”

Section: Outlier Detection Methods Based On Isolated Forestmentioning

confidence: 99%

“…𝑖,𝑗 = 𝑤𝑣 𝑡 𝑖,𝑗 + 𝑐 1 (𝐺 𝑡 𝑗 − 𝑦 𝑡 𝑖,𝑗 ) + 𝑐 2 (𝑃 𝑡 𝑖,𝑗 − 𝑦 𝑡 𝑖,𝑗 )𝑟 2 (21) where 𝑣 𝑡+1 𝑖,𝑗 shows the 𝑖 th chameleon's new velocity in the 𝑗th dimension of the iteration 𝑡 + 1, and 𝑣 𝑡 𝑖,𝑗 shows the 𝑖 th chameleon's current velocity in the 𝑗 th dimension.…”

Section: 𝑣 𝑡+1mentioning

confidence: 99%

Innovative Framework for Thyroid Disease Detection by Leveraging Hybrid AGTEO Feature Selection and GRU Classification Model

2024

Int. Res. J. multidiscip. Technovation

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Thyroid disease remains a significant health concern, necessitating advanced diagnostic tools for swift and accurate identification. The initial step involves preprocessing datasets, employing an Outlier Detection Method with Isolated Forest in conjunction with data normalization techniques to eliminate noise and standardize the data, laying a robust groundwork for subsequent analysis. Subsequently, feature extraction is conducted utilizing an Enhanced AlexNet architecture augmented by a more intricate Chameleon Swarm Algorithm (CSA) model to discern finer patterns within the data, enhancing the discriminative nature of the extracted features. Following this, a feature selection strategy employing hybrid optimization is deployed, amalgamating the strengths of Equilibrium Optimizer and Artificial Gorilla Troops Optimizer (AGTO) into a hybrid model named HAGTEO, aiming to identify the most informative features, thus reducing dimensionality and enhancing classifier efficiency. Ultimately, the Gated Recurrent Unit (GRU) classifier is employed for thyroid disease classification based on the extracted and selected features. Renowned for its capability to capture temporal dependencies, the GRU model further enhances classification accuracy. The proposed framework is subjected to testing on two distinct datasets, demonstrating its efficacy in thyroid disease detection. Experimental outcomes reveal superior performance compared to conventional methods, achieving accuracies of 98.07% and 98.00% for dataset 1 and dataset 2, respectively. As an advanced diagnostic solution for thyroid disease, it holds promising potential.

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“…Convolutional neural networks can automatically extract and learn features from one-dimensional sequence data very effectively [6] . [7] Based on the comprehensive wavelet decomposition, autoregressive integrated moving average ( ARIMA ) and gated recurrent unit ( GRU ) model, it has better prediction accuracy, stability and robustness for conventional water quality indicators. [8] A deep hybrid model based on convolutional neural network-gated recurrent unit-support vector regression ( CNN-GRU-SVR ) was used to predict the water quality of the Ganges River using historical data.…”

Section: Introductionmentioning

confidence: 99%

Research on water quality prediction based on PE-CNN-GRU hybrid model

et al. 2023

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Sewage treatment is a complex and nonlinear process. In this paper, a prediction method based on convolutional neural network (CNN) and gated recurrent unit (GRU) hybrid neural network is proposed for the prediction of dissolved oxygen concentration in sewage treatment. Firstly, akima 's method is used to complete the filling preprocessing of missing data, and then the integrated empirical mode decomposition (EEMD) algorithm is used to denoise the key factors of water quality data. Pearson correlation analysis is used to select better water quality parameters as the input of the model. Then, CNN is used to convolve the data sequence to extract the feature components of sewage data. Then, the CNN-GRU hybrid network is used to extract the feature components for sequence prediction, and then the predicted output value is obtained. The mean absolute error (MAE), root mean square error (RMSE) and mean square error (MSE) were used as evaluation criteria to analyze the prediction results of the model. By comparing with RNN model, LSTM model, GRU model and CNN-LSTM model, the results show that the PCA-EEMD-CNN-GRU (PE-CNN-GRU) hybrid model proposed in this paper has significantly improved the prediction accuracy of dissolved oxygen concentration.

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A Hybrid Prediction Framework for Water Quality with Integrated W-ARIMA-GRU and LightGBM Methods

Cited by 13 publications

References 36 publications

Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches

Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches

Innovative Framework for Thyroid Disease Detection by Leveraging Hybrid AGTEO Feature Selection and GRU Classification Model

Research on water quality prediction based on PE-CNN-GRU hybrid model

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