Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks

He, Sheng; Sang, Xuefeng; Yin, Jun; Zheng, Yang; Chen, Heting

doi:10.1007/s11269-022-03401-z

Cited by 20 publications

(4 citation statements)

References 41 publications

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“…With the development of artificial intelligence, based on machine learning, forecasting techniques have been divided into shallow and deep learning models. Among them, the shallow learning model includes extreme learning machine (ELM) [9]- [11], back propagation (BP) neural network [12]- [14], and support vector machine (SVM) [15]- [17] Although it can learn nonlinear features adaptively, owing to the limited structure of the shallow learning model, it is prone to problems such as local optimization, poor convergence, and overfitting. Currently, deep learning models are widely applied in wind power forecasting [18]- [22].…”

Section: A Related Workmentioning

confidence: 99%

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

Zhang,

2024

IEEE Access

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The escalating incorporation of wind energy into power grids imposes constraints on the effective operation and control of power systems. Effective short-term wind power forecasting technology is essential for safe power supply and stable operation of the power grid. Thus, a UMAP-IVMD-ILSTM model for wind power forecasting is proposed based on the uniform manifold approximation and projection (UMAP) integrated improved variational mode decomposition (IVMD) and multi-level improved long and short-term memory (LSTM) networks. First, the UMAP approach was utilized to reduce the dimension of related meteorological indicators, and k-means clustering was applied to classify the weather categories after dimension reduction. Subsequently, through the improved variational mode decomposition, the wind power of the adjacent day is decomposed. Moreover, long and short-term memory networks were optimized using the sparrow search algorithm (SSA) as an improved LSTM (ILSTM), and a wind power forecasting model with multi-level LSTM was established for each mode, and the forecasting results of each mode were integrated. Through actual wind farm operation data, it was proven that the method proposed in this study is effective in processing high-dimensional numerical weather prediction (NWP) data. The model can better preserve the information of the original data and reduce the complexity of the original data sequence based on visualization. Compared with the comparative models, the proposed model reduces the RMSE and MAE by up to 18.2% and 21.6% respectively, and has the least running time, which can effectively improve the accuracy and efficiency of wind power prediction.

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Section: A Related Workmentioning

confidence: 99%

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

Zhang,

2024

IEEE Access

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“…The LSTM model is utilized to obtain the findings after the MFCC model has been used for feature extraction [35]. The fact that LSTM outperforms a few competing models with an ICBHI score of 74% after being pitted against them shows the power of the LSTM-based framework in lung sound data pre-processing [2,30].…”

Section: Related Work Historymentioning

confidence: 99%

“…The MFCC method is used to convert signals into spectral images. ResNet101 and VGG16 are two feature extractors with DCNN classifiers [28][29][30]. This approach incorporated CNN classification, respiratory sound, and pre-trained image recognition.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning based Diagnosis and Analysis of Lung Sound Aberrations

Gulzar¹,

Li²,

Manzoor³

et al. 2023

IJBB

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With the development of computer -systems that can collect and analyze enormous volumes of data, the medical profession is establishing several non-invasive tools. This work attempts to develop a non-invasive technique for identifying respiratory sounds acquired by a stethoscope and voice recording software via machine learning techniques. This study suggests a trained and proven CNN-based approach for categorizing respiratory sounds. A visual representation of each audio sample is constructed, allowing resource identification for classification using methods like those used to effectively describe visuals. We used a technique called MelFrequency Cepstral Coefficients (MFCCs). Here, features are retrieved and categorized via VGG16 (transfer learning) and prediction is accomplished using 5-fold cross-validation. Employing various data splitting techniques, Respiratory Sound Database obtained cutting- edge results, including accuracy of 95%, precision of 88%, recall score of 86%, and F1 score of 81%. The ICBHI dataset is used to train and test the model.

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“…where w ij , b j , f, m, n, h j , Q p , w jk and b jp are the weight of the hidden layer, the bias of the hidden layer, the activation function, the total number of input layers, the number of neurons in the hidden layer, the output of the hidden layer, the output of the output layer, the weight of the output layer and the bias of the output layer, respectively [45].…”

Section: Bp Modelmentioning

confidence: 99%

A Study of a Model for Predicting Pneumatic Subsoiling Resistance Based on Machine Learning Techniques

et al. 2023

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In order to explore the drag reduction mechanism of pneumatic subsoiling and study the influence of pneumatic subsoiling on the soil, this study used machine learning models to predict the working resistance of a pneumatic subsoiler and adopted random forest (RF), error back-propagation (BP), eXtreme gradient boosting (XGBoost) and support vector regression (SVR) to analyze and compare the predictions of these four models. Field experiments were carried out in two fields with different bulk densities and moisture content. The effects of these parameters on the resistance of pneumatic subsoiling were studied by changing the working air pressure, depth and forward speed. In the RF, SVR, XGBoost and BP models, five parameters (working air pressure, working depth, forward speed, bulk density and moisture content) were inputted as independent variables, and the operating resistance of pneumatic subsoiling was used as the predicted value. After training the four models, the results showed that the R2 value of the RF model was the highest and the error was the smallest, which made it better than the SVR, XGBoost and BP models. The values of MAPE, R2 and RMSE for the RF model’s test set were 0.01, 0.99, and 3.61 N, respectively, indicating that the RF model could predict the resistance value of subsoiling well. When the RF model was used to analyze the five input parameters, the experimental results showed that the contribution of working air pressure to reducing the resistance of subsoiling reached 29%, indicating that pneumatic subsoiling can reduce the resistance, drag and consumption.

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Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks

Cited by 20 publications

References 41 publications

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

Transfer Learning based Diagnosis and Analysis of Lung Sound Aberrations

A Study of a Model for Predicting Pneumatic Subsoiling Resistance Based on Machine Learning Techniques

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