A GA-BP Neural Network Regression Model for Predicting Soil Moisture in Slope Ecological Protection

Liu, Dunwen; Liu, Chao; Tang, Yu; Gong, Cheng-Bin

doi:10.3390/su14031386

Cited by 21 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On this basis, the metaheuristic algorithm is utilized to traverse the entire space of the model to obtain the optimal solution of the model. It has been successfully applied to many tasks, such as neuroevolutionary algorithms for optimizing deep reinforcement learning models [20], genetic algorithms (GA) for optimizing BP neural networks [21], and evolutionary algorithms (EA) for optimizing the hyperparameters of neural networks [22]. In a recent prediction study, Xie Hailun et al proposed a prediction model based on an improved gray wolf optimization (GWO) algorithm optimizing CNN-LSTM, which enhances the optimization finding ability of the GWO algorithm by introducing a different searching mechanism, and the optimized CNN-LSTM network provides a better characterization ability, which is not only capable of capturing the interactions of the important features but also capable of encapsulating the complex temporal complex dependencies in the background for time series tasks [23].…”

Section: Introduction 1literature Reviewmentioning

confidence: 99%

A boiler oxygen content and furnace temperature prediction model based on honey badger algorithm optimized neural network

Ji,

Tao,

Zhang

2024

Eng. Res. Express

View full text Add to dashboard Cite

Aiming at the problem that it is difficult to accurately predict the oxygen content and furnace temperature during the combustion process inside the circulating fluidized bed boiler, this paper proposes a prediction model with a mixture of the convolutional neural network(CNN), bi-directional long-short term memory network(biLSTM), and honey badger algorithm(HBA). First, process variables related to furnace temperature and oxygen content were selected as input variables using actual test data from circulating fluidized bed boilers. Secondly, the parsed input variables are analyzed by CNN through an analytical mechanism to extract the data into smaller details and features, which are used as inputs to the biLSTM layer. The BiLSTM layer selectively memorizes the information of the input temporal data and learns the information dependence of the temporal data sequences, which then solves the temporal problem of the training of the irregular trend of the oxygen content and the furnace temperature. Finally, the HBA is utilized to continuously traverse and search the CNN-biLSTM model to find the optimal parameters of the model. The experimental results show: the CNN-biLSTM neural network model mixed with the HBA is able to accurately predict the oxygen content and furnace temperature. The experimental results show that the CNN-biLSTM neural network model with hybrid HBA is able to accurately predict the oxygen content and furnace temperature, and the average MAPE errors for the oxygen content are HBA-CNN-biLSTM (2.92E-03), CNN (7.96E-02), LSTM (5.13E-02), GRU (4.83E-02), RF (4.96E-02), RBF (8.41E-02), SVM (5.71E-02), RNN ( 5.53E-02), CNN-LSTM (4.79E-02).

show abstract

Section: Introduction 1literature Reviewmentioning

confidence: 99%

A boiler oxygen content and furnace temperature prediction model based on honey badger algorithm optimized neural network

Ji,

Tao,

Zhang

2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…Recent studies have applied deep learning technology to overcome the problem of low prediction accuracy of soil moisture due to irregular and complex characteristics of soil [ 16 , 17 ]. Deep neural network regression (DNNR) has been used to predict average soil moisture per day [ 18 ]. The soil moisture was predicted by applying an artificial neural network (ANN), convolution neural network (CNN), deep belief network (DBN), autoencoders (AEs), extreme learning machines (ELMs), and long short-term memory (LSTM) techniques [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a Soil Moisture Prediction Model Based on Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) in Soybean Cultivation

Park

Lee

Kim

et al. 2023

Sensors

View full text Add to dashboard Cite

Due to climate change, soil moisture may increase, and outflows could become more frequent, which will have a considerable impact on crop growth. Crops are affected by soil moisture; thus, soil moisture prediction is necessary for irrigating at an appropriate time according to weather changes. Therefore, the aim of this study is to develop a future soil moisture (SM) prediction model to determine whether to conduct irrigation according to changes in soil moisture due to weather conditions. Sensors were used to measure soil moisture and soil temperature at a depth of 10 cm, 20 cm, and 30 cm from the topsoil. The combination of optimal variables was investigated using soil moisture and soil temperature at depths between 10 cm and 30 cm and weather data as input variables. The recurrent neural network long short-term memory (RNN-LSTM) models for predicting SM was developed using time series data. The loss and the coefficient of determination (R2) values were used as indicators for evaluating the model performance and two verification datasets were used to test various conditions. The best model performance for 10 cm depth was an R2 of 0.999, a loss of 0.022, and a validation loss of 0.105, and the best results for 20 cm and 30 cm depths were an R2 of 0.999, a loss of 0.016, and a validation loss of 0.098 and an R2 of 0.956, a loss of 0.057, and a validation loss of 2.883, respectively. The RNN-LSTM model was used to confirm the SM predictability in soybean arable land and could be applied to supply the appropriate moisture needed for crop growth. The results of this study show that a soil moisture prediction model based on time-series weather data can help determine the appropriate amount of irrigation required for crop cultivation.

show abstract

“…The outcome of this research serves as a crucial foundation for regulating the deposited layers and selecting optimal process parameters. Given the inherent nature of laser-arc hybrid additive manufacturing as a predominantly small sample experiment, the widely employed Backpropagation neural network (BPNN) algorithm is found to be inadequate due to its susceptibility to overfitting when confronted with limited sample data [14]. Conversely, the Support Vector Regression (SVR) algorithm demonstrates enhanced stability, robustness, and generalization capabilities for small sample low-dimensional problems [15].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Deposition Layer Morphology Dimensions Based on PSO-SVR for Laser–arc Hybrid Additive Manufacturing

Wang

Liu

et al. 2023

Coatings

View full text Add to dashboard Cite

Laser–arc composite additive manufacturing holds significant potential for a wide range of industrial applications, and the control of morphological dimensions in the deposited layer is a critical aspect of this technology. The width and height dimensions within the deposited layer of laser–arc hybrid additive manufacturing serve as essential indicators of its morphological characteristics, directly influencing the shape quality of the deposited layer. Accurate prediction of the shape dimensions becomes crucial in providing effective guidance for size control. To achieve precise prediction of shape dimensions in laser–arc composite additive manufacturing and ensure effective regulation of the deposited layer’s shape quality, this study introduces a novel approach that combines a particle swarm algorithm (PSO) with an optimized support vector regression (SVR) technique. By optimizing the SVR parameters through the PSO algorithm, the SVR model is enhanced and fine-tuned to accurately predict the shape dimensions of the deposited layers. In this study, a series of 25 laser–arc hybrid additive manufacturing experiments were conducted to compare different approaches. Specifically, the SVR model was built using selected radial basis function (rbf) kernel functions. Furthermore, the penalty factors and kernel parameters of the SVR model were optimized using the particle swarm optimization (PSO) algorithm, leading to the development of a PSO-SVR prediction model for the morphological dimensions of the deposited layers. The performance of the PSO-SVR model was compared with that of the SVR, BPNN, and LightGBM models. Model accuracy was evaluated using a test set, revealing average relative errors of 2.39%, 7.719%, 9.46%, and 5.356% for the PSO-SVR, SVR, BPNN, and LightGBM models, respectively. The PSO-SVR model exhibited excellent prediction accuracy with minimal fluctuations in prediction error. This performance demonstrates the model’s ability to effectively capture the intricate and non-linear relationship between process parameters and deposition layer dimensions. Consequently, the PSO-SVR model can provide a foundation for the control of morphological dimensions in the deposition layer, offering an effective guide for deposition layer morphology dimension control in laser–arc composite additive manufacturing.

show abstract

A GA-BP Neural Network Regression Model for Predicting Soil Moisture in Slope Ecological Protection

Cited by 21 publications

References 29 publications

A boiler oxygen content and furnace temperature prediction model based on honey badger algorithm optimized neural network

A boiler oxygen content and furnace temperature prediction model based on honey badger algorithm optimized neural network

Development of a Soil Moisture Prediction Model Based on Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) in Soybean Cultivation

Prediction of Deposition Layer Morphology Dimensions Based on PSO-SVR for Laser–arc Hybrid Additive Manufacturing

Contact Info

Product

Resources

About