Predicting Cutterhead Torque for TBM based on Different Characteristics and AGA-Optimized LSTM-MLP

The Manasi region is located in an arid and semi-arid region with fragile ecology and scarce resources. The land use change prediction is important for the management and optimization of land resources. We utilized Sankey diagram, dynamic degree of land use, and landscape indices to explore the temporal and spatial variation of land use and integrated the LSTM and MLP algorithms to predict land use prediction. The MLP-LSTM prediction model retains the spatiotemporal information of land use data to the greatest extent and extracts the spatiotemporal variation characteristics of each grid through a training set. Results showed that (1) from 1990 to 2020, cropland, tree cover, water bodies, and urban areas in the Manasi region increased by 855.3465 km2, 271.7136 km2, 40.0104 km2, and 109.2483 km2, respectively, whereas grassland and bare land decreased by 677.7243 km2 and 598.5945 km2, respectively; (2) Kappa coefficients reflect the accuracy of the mode’s predictions in terms of quantity. The Kappa coefficients of the land use data predicted by the MLP-LSTM, MLP-ANN, LR, and CA-Markov models were calculated to be 95.58%, 93.36%, 89.48%, and 85.35%, respectively. It can be found that the MLP-LSTM and MLP-ANN models obtain higher accuracy in most levels, while the CA–Markov model has the lowest accuracy. (3) The landscape indices can reflect the spatial configuration characteristics of landscape (land use types), and evaluating the prediction results of land use models using landscape indices can reflect the prediction accuracy of the models in terms of spatial features. The results indicate that the model predicted by MLP-LSTM model conforms to the development trend of land use from 1990 to 2020 in terms of spatial features. This gives a basis for the study of the Manasi region to formulate relevant land use development and rationally allocate land resources.

show abstract

Spatiotemporal change and prediction of land use in Manasi region based on deep learning

Wang

Yin

Liu

et al. 2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

Multivariable real-time prediction method of tunnel boring machine operating parameters based on spatio-temporal feature fusion

Pang,

Hua,

et al. 2024

Advanced Engineering Informatics

View full text Add to dashboard Cite

A multi-channel decoupled deep neural network for tunnel boring machine torque and thrust prediction

Qin

Tao

et al. 2023

Tunnelling and Underground Space Technology

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Predicting Cutterhead Torque for TBM based on Different Characteristics and AGA-Optimized LSTM-MLP

Cited by 3 publications

References 10 publications

Spatiotemporal change and prediction of land use in Manasi region based on deep learning

Spatiotemporal change and prediction of land use in Manasi region based on deep learning

Multivariable real-time prediction method of tunnel boring machine operating parameters based on spatio-temporal feature fusion

A multi-channel decoupled deep neural network for tunnel boring machine torque and thrust prediction

Contact Info

Product

Resources

About