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A platform for research: civil engineering, architecture and urbanism
Prediction of landslide deformation using back-propagation neural network
This study demonstrates application of analysis of geotechnical sensors based data for landslide monitoring and prediction at Tangni landslide site, 12 km from Pipalkoti along Rishikesh — Badrinath highway (NH-58) in India. Prediction of slope deformations is carried out using back propagation neural network (BPNN) model. Training of the model is done using 60% of the data received from the landslide site while 20% of data is used for testing and finally 20% data is used for validation of the model. The application of the BPNN based model is to predict the slope deformations using daily and antecedent rainfall as input variables. It is found using hit and trial method that a back propagation neural network having four layers with two hidden layers and twelve neurons is performing in an optimal manner on data received from Tangni landslide. The results of the study suggest a promising BPNN model that produces an accuracy of approximately 95% in predicting the slope deformation.
Prediction of landslide deformation using back-propagation neural network
This study demonstrates application of analysis of geotechnical sensors based data for landslide monitoring and prediction at Tangni landslide site, 12 km from Pipalkoti along Rishikesh — Badrinath highway (NH-58) in India. Prediction of slope deformations is carried out using back propagation neural network (BPNN) model. Training of the model is done using 60% of the data received from the landslide site while 20% of data is used for testing and finally 20% data is used for validation of the model. The application of the BPNN based model is to predict the slope deformations using daily and antecedent rainfall as input variables. It is found using hit and trial method that a back propagation neural network having four layers with two hidden layers and twelve neurons is performing in an optimal manner on data received from Tangni landslide. The results of the study suggest a promising BPNN model that produces an accuracy of approximately 95% in predicting the slope deformation.
Prediction of landslide deformation using back-propagation neural network
Chaturvedi, Pratik (author) / Srivastava, Shikha (author) / Tyagi, Neetu (author)
2015-12-01
396180 byte
Conference paper
Electronic Resource
English
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