Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine

2008 ◽  
Vol 29 (4) ◽  
pp. 583-603 ◽  
Author(s):  
Aavudai Anandhi ◽  
V. V. Srinivas ◽  
D. Nagesh Kumar ◽  
Ravi S. Nanjundiah
Keyword(s):  
Support Vector Machine ◽  
Surface Temperature ◽  
River Basin ◽  
Support Vector ◽  
Sres Scenarios ◽  
Ipcc Sres ◽  
Ipcc Sres Scenarios

Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine

2008 ◽  
Vol 28 (3) ◽  
pp. 401-420 ◽  
Author(s):  
Aavudai Anandhi ◽  
V. V. Srinivas ◽  
Ravi S. Nanjundiah ◽  
D. Nagesh Kumar
Keyword(s):  
Support Vector Machine ◽  
River Basin ◽  
Support Vector ◽  
Sres Scenarios ◽  
Ipcc Sres ◽  
Ipcc Sres Scenarios

Daily relative humidity projections in an Indian river basin for IPCC SRES scenarios

2011 ◽  
Vol 108 (1-2) ◽  
pp. 85-104 ◽  
Author(s):  
Aavudai Anandhi ◽  
V. V. Srinivas ◽  
D. Nagesh Kumar ◽  
Ravi S. Nanjundiah
Keyword(s):  
Relative Humidity ◽  
River Basin ◽  
Indian River ◽  
Sres Scenarios ◽  
Ipcc Sres ◽  
Daily Relative Humidity ◽  
Ipcc Sres Scenarios

scholarly journals Copper Demand and Supply Simulation in 21st Century Based on the IPCC SRES Scenarios

2004 ◽  
Vol 120 (12) ◽  
pp. 681-687 ◽  
Author(s):  
Koji TOKIMATSU ◽  
Toshihide ITO ◽  
Takayoshi SHINKUMA ◽  
Kazuhiko FURUKAWA ◽  
Toshisuke OGIWARA ◽  
...  
Keyword(s):  
21St Century ◽  
Demand And Supply ◽  
Sres Scenarios ◽  
Ipcc Sres ◽  
Ipcc Sres Scenarios

scholarly journals Intrinsic limits to predictability of abrupt regional climate change in IPCC SRES scenarios

2002 ◽  
Vol 29 (16) ◽  
pp. 14-1-14-4 ◽  
Author(s):  
M. Schaeffer ◽  
F. M. Selten ◽  
J. D. Opsteegh ◽  
H. Goosse
Keyword(s):  
Climate Change ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Sres Scenarios ◽  
Ipcc Sres ◽  
Ipcc Sres Scenarios

scholarly journals Modelling the Vegetation Response to Climate Changes in the Yarlung Zangbo River Basin Using Random Forest

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1433 ◽  
Author(s):  
Kaige Chi ◽  
Bo Pang ◽  
Lizhuang Cui ◽  
Dingzhi Peng ◽  
Zhongfan Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
River Basin ◽  
Partial Correlation ◽  
Climatic Factors ◽  
Vegetation Coverage ◽  
Support Vector ◽  
Partial Correlation Analysis ◽  
Proposed Model ◽  
Yarlung Zangbo River

Vegetation coverage variation may influence watershed water balance and water resource availability. Yarlung Zangbo River, the longest river on the Tibetan Plateau, has high spatial heterogeneity in vegetation coverage and is the main freshwater resource of local residents and downstream countries. In this study, we proposed a model based on random forest (RF) to predict the Normalized Difference Vegetation Index (NDVI) of the Yarlung Zangbo River Basin and explore its relationship with climatic factors. High-resolution datasets of NDVI and monthly meteorological observation data from 2000 to 2015 were used to calibrate and validate the proposed model. The proposed model was then compared with artificial neural network and support vector machine models, and principal component analysis and partial correlation analysis were also used for predictor selection of artificial neural network and support vector machine models for comparative study. The results show that RF had the highest model efficiency among the compared models. The Nash–Sutcliffe coefficients of the proposed model in the calibration period and verification period were all higher than 0.8 for the five subzones; this indicated that the proposed model can successfully simulate the relationship between the NDVI and climatic factors. By using built-in variable importance evaluation, RF chose appropriate predictor combinations without principle component analysis or partial correlation analysis. Our research is valuable because it can be integrated into water resource management and elucidates ecological processes in Yarlung Zangbo River Basin.

Sign in / Sign up

Export Citation Format

Share Document