Study on Identification Method of Tool Wear Based on Singular Spectrum Analysis and Support Vector Machine

2011 ◽  
Author(s):  
Peng Nie ◽  
Zhengqiang Li ◽  
Yanchun Liu ◽  
Xinyu Liu ◽  
Hongyao Xu
Keyword(s):  
Support Vector Machine ◽  
Tool Wear ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Support Vector ◽  
Identification Method ◽  
Singular Spectrum

scholarly journals Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting

2020 ◽  
Vol 10 (9) ◽  
pp. 3224 ◽  
Author(s):  
Pa Ousman Bojang ◽  
Tao-Chang Yang ◽  
Quoc Bao Pham ◽  
Pao-Shan Yu
Keyword(s):  
Machine Learning ◽  
Water Resources ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Hybrid Models ◽  
Monthly Rainfall ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Singular Spectrum ◽  
Standard Models

Monthly rainfall forecasts can be translated into monthly runoff predictions that could support water resources planning and management activities. Therefore, development of monthly rainfall forecasting models in reservoir watersheds is essential for generating future rainfall amounts as an input to a water-resources-system simulation model to predict water shortage conditions. This research aims to examine the reliability of linking a data preprocessing method (singular spectrum analysis, SSA) with machine learning, least-squares support vector regression (LS-SVR), and random forest (RF), for monthly rainfall forecasting in two reservoir watersheds (Deji and Shihmen reservoir watersheds) located in Taiwan. Merging SSA with LS-SVR and RF, the hybrid models (SSA-LSSVR and SSA-RF) were developed and compared with the standard models (LS-SVR and RF). The proposed models were calibrated and validated using the watersheds’ observed areal monthly rainfalls separated into 70 percent of data for calibration and 30 percent of data for validation. Model performances were evaluated using two accuracy measures, root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). Results show that the hybrid models could efficiently forecast monthly rainfalls. Nonetheless, the performances of the hybrid models vary in both watersheds which suggests that prior knowledge about the watershed’s hydrological behavior would be helpful to implement the appropriate model. Overall, the hybrid models significantly surpass the standard models for the two studied watersheds, which indicates that the proposed models are a prudent modeling approach that could be employed in the current research regions for monthly rainfall forecasting.

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