scholarly journals Estimation of Water-Use Rates Based on Hydro-Meteorological Variables Using Deep Belief Network

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2700
Author(s):  
Jang Hyun Sung ◽  
Young Ryu ◽  
Eun-Sung Chung
Keyword(s):  
Water Use ◽  
Potential Evapotranspiration ◽  
Stream Water ◽  
Weather Forecast ◽  
Forecast Model ◽  
Parameter Tuning ◽  
Deep Belief Network ◽  
Meteorological Variables ◽  
Belief Network ◽  
Highly Correlated

This study proposed a deep learning-based model to estimate stream water-use rate (WUR) using precipitation (P) and potential evapotranspiration (PET). Correlations were explored to identify relationships among accumulated meteorological variables for various time durations (three-, four-, five-, and six-month cumulative) and WUR, which revealed that three-month cumulative meteorological variables and WUR were highly correlated. A deep belief network (DBN) based on iterating parameter tuning was developed to estimate WUR using P, PET, and antecedent stream water-use rate (DWUR). The training and validation periods were 2011–2016, and 2017–2019, respectively. The results showed that the PET-DWUR based model provided better performances in Nash–Sutcliff efficiency (NSE), root mean square error (RMSE), and determination coefficient (R2) than the P-PET-DWUR and P-DWUR models. The framework in this study can provide a forecast model for deficiencies of stream water use coupled with a weather forecast model.

scholarly journals A Novel Deep Belief Network Model Constructed by Improved Conditional RBMs and its Application in RUL Prediction for Hydraulic Pumps

2020 ◽  
Vol 25 (3) ◽  
pp. 373-382
Author(s):  
He Yu ◽  
Zaike Tian ◽  
Hongru Li ◽  
Baohua Xu ◽  
Guoqing An
Keyword(s):  
Deep Learning ◽  
Graph Model ◽  
Parameter Tuning ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Deep Feature ◽  
Proposed Model ◽  
Vector Machines ◽  
Deep Feature Extraction

Residual Useful Life (RUL) prediction is a key step of Condition-Based Maintenance (CBM). Deep learning-based techniques have shown wonderful prospects on RUL prediction, although their performances depend on heavy structures and parameter tuning strategies of these deep-learning models. In this paper, we propose a novel Deep Belief Network (DBN) model constructed by improved conditional Restrict Boltzmann Machines (RBMs) and apply it in RUL prediction for hydraulic pumps. DBN is a deep probabilistic digraph neural network that consists of multiple layers of RBMs. Since RBM is an undirected graph model and there is no communication among the nodes of the same layer, the deep feature extraction capability of the original DBN model can hardly ensure the accuracy of modeling continuous data. To address this issue, the DBN model is improved by replacing RBM with the Improved Conditional RBM (ICRBM) that adds timing linkage factors and constraint variables among the nodes of the same layers on the basis of RBM. The proposed model is applied to RUL prediction of hydraulic pumps, and the results show that the prediction model proposed in this paper has higher prediction accuracy compared with traditional DBNs, BP networks, support vector machines and modified DBNs such as DEBN and GC-DBN.

Software Defect Prediction via Deep Belief Network

2019 ◽  
Vol 28 (5) ◽  
pp. 925-932
Author(s):  
Hua WEI ◽  
Chun SHAN ◽  
Changzhen HU ◽  
Yu ZHANG ◽  
Xiao YU
Keyword(s):  
Deep Belief Network ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Belief Network ◽  
Software Defect

scholarly journals Research on Deep Belief Network of Wind Power Control Management Unit Based on Attack Identification

2020 ◽  
Vol 1646 ◽  
pp. 012120
Author(s):  
Wei Liu ◽  
Zhiwei Huang ◽  
Rui Chen ◽  
Kai Ding ◽  
Xiaofan Zhu ◽  
...  
Keyword(s):  
Power Control ◽  
Wind Power ◽  
Deep Belief Network ◽  
Management Unit ◽  
Belief Network ◽  
Control Management
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