scholarly journals Long-term Daily Rainfall Pattern Recognition: Application of Principal Component Analysis

2015 ◽  
Vol 30 ◽  
pp. 127-132 ◽  
Author(s):  
Melawani Othman ◽  
Zulfa Hanan Ash’aari ◽  
Nur Diyana Mohamad
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Daily Rainfall ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Pattern

scholarly journals Bidimensional and Multidimensional Principal Component Analysis in Long Term Atmospheric Monitoring

Atmosphere ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 155 ◽  
Author(s):  
Barbara Giussani ◽  
Simone Roncoroni ◽  
Sandro Recchia ◽  
Andrea Pozzi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Atmospheric Monitoring

Long-term runoff forecast using BP neural network based on climatic factors and mutual information method in the Qujiang Catchment of China

2020 ◽  
Author(s):  
Huihui Dai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Mutual Information ◽  
Bp Neural Network ◽  
Climatic Factors ◽  
Principal Component ◽  
Component Analysis ◽  
Information Method ◽  
Runoff Forecast

<p>The formation of runoff is extremely complicated, and it is not good enough to forecast the future runoff only by using the previous runoff or meteorological data. In order to improve the forecast precision of the medium and long-term runoff forecast model, a set of forecast factor group is selected from meteorological factors, such as rainfall, temperature, air pressure and the circulation factors released by the National Meteorological Center  using the method of mutual information and principal component analysis respectively. Results of the forecast in the Qujiang Catchment suggest the climatic factor-based BP neural network hydrological forecasting model has a better forecasting effect using the mutual information method than using the principal component analysis method.</p>

Pattern Recognition on X-ray Fluorescence Records from Copenhagen Lake Sediments Using Principal Component Analysis

2014 ◽  
Vol 225 (12) ◽  
Author(s):  
Norman Schreiber ◽  
Emanuel Garcia ◽  
Aart Kroon ◽  
Peter C. Ilsøe ◽  
Kurt H. Kjær ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Lake Sediments ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray

scholarly journals Forcing variables in simulation of transpiration of water stressed plants determined by principal component analysis

2016 ◽  
Vol 30 (4) ◽  
pp. 431-445
Author(s):  
Angelica Durigon ◽  
Quirijn de Jong van Lier ◽  
Klaas Metselaar
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Specific Humidity ◽  
Leaf Angle ◽  
Prediction Errors ◽  
Angle Distribution ◽  
Time Frequency ◽  
Soil Dryness

AbstractTo date, measuring plant transpiration at canopy scale is laborious and its estimation by numerical modelling can be used to assess high time frequency data. When using the model by Jacobs (1994) to simulate transpiration of water stressed plants it needs to be reparametrized. We compare the importance of model variables affecting simulated transpiration of water stressed plants. A systematic literature review was performed to recover existing parameterizations to be tested in the model. Data from a field experiment with common bean under full and deficit irrigation were used to correlate estimations to forcing variables applying principal component analysis. New parameterizations resulted in a moderate reduction of prediction errors and in an increase in model performance. Agsmodel was sensitive to changes in the mesophyll conductance and leaf angle distribution parameterizations, allowing model improvement. Simulated transpiration could be separated in temporal components. Daily, afternoon depression and long-term components for the fully irrigated treatment were more related to atmospheric forcing variables (specific humidity deficit between stomata and air, relative air humidity and canopy temperature). Daily and afternoon depression components for the deficit-irrigated treatment were related to both atmospheric and soil dryness, and long-term component was related to soil dryness.

Sign in / Sign up

Export Citation Format

Share Document