Articles | Volume 23, issue 1
https://doi.org/10.5194/npg-23-13-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/npg-23-13-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Jan 2016
Research article |
| 27 Jan 2016
Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America
T. Soares dos Santos
CORRESPONDING AUTHOR
Federal University of Rio Grande do Norte, Campus Universitário
Lagoa Nova, Natal, RN, 59078-970, Brazil
D. Mendes
Federal University of Rio Grande do Norte, Campus Universitário
Lagoa Nova, Natal, RN, 59078-970, Brazil
R. Rodrigues Torres
Federal University of
Itajubá, Instituto de Recursos Naturais, Av. BPS, 1303, Pinheirinho,
Itajubá, MG, 37500-903, Brazil
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22 citations as recorded by crossref.
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- Uncertainties in projections of climate extremes indices in South America via Bayesian inference C. Gouveia et al. 10.1002/joc.7650
- Spatial and temporal rainfall variability and its controlling factors under an arid climate condition: case of Gabes Catchment, Southern Tunisia S. Jemai et al. 10.1007/s10668-021-01668-7
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- Climate projections and downscaling techniques: a discussion for impact studies in urban systems M. Smid & A. Costa 10.1080/12265934.2017.1409132
- Seasonal probabilistic precipitation prediction in Comahue region (Argentina) using statistical techniques M. González et al. 10.1007/s00704-022-04324-w
- Multinomial logistic regression method for early detection of autism spectrum disorders D. Jayaprakash & C. Kanimozhiselvi 10.1016/j.measen.2024.101125
- Picea schrenkiana tree ring blue intensity reveal recent glacier mass loss in High Mountain Asia is unprecedented within the last four centuries W. Yue et al. 10.1016/j.gloplacha.2023.104210
- Enhancing short-term forecasting of daily precipitation using numerical weather prediction bias correcting with XGBoost in different regions of China J. Dong et al. 10.1016/j.engappai.2022.105579
- MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region E. Gomes et al. 10.1007/s10661-022-09939-0
- Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests . Balaji Prabhu B. V. & M. Dakshayini 10.4018/IJGHPC.2020100103
- Machine Learning for Hydropower Scheduling: State of the Art and Future Research Directions C. Bordin et al. 10.1016/j.procs.2020.09.190
- Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin R. Xu et al. 10.1155/2020/8680436
- Development of Precipitation Forecast Model Based on Artificial Intelligence and Subseasonal Clustering L. Parviz & K. Rasouli 10.1061/(ASCE)HE.1943-5584.0001862
- Predicting saturated and near-saturated hydraulic conductivity using artificial neural networks and multiple linear regression in calcareous soils H. Mozaffari et al. 10.1371/journal.pone.0296933
- Regional hydroclimatic projection using an coupled composite downscaling model with statistical bias corrector B. Kang & S. Moon 10.1007/s12205-017-1176-7
- Rainfall-related natural disasters in the Northeast of Brazil as a response to ocean-atmosphere interaction B. Silva et al. 10.1007/s00704-019-02930-9
- Bireysel Tüketici İhtiyaç Kredisi Talep Tahminlerinin Vektör Oto Regresyon ve Yapay Sinir Ağı Modelleri ile Karşılaştırmalı Analizi İ. ERTUĞRUL & A. ÖZÇİL 10.14784/marufacd.305559
- Medium-Term Rainfall Forecasts Using Artificial Neural Networks with Monte-Carlo Cross-Validation and Aggregation for the Han River Basin, Korea J. Lee et al. 10.3390/w12061743
20 citations as recorded by crossref.
- Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon E. Pinheiro Gomes et al. 10.1007/s00382-024-07200-7
- Daily rainfall estimates considering seasonality from a MODWT-ANN hybrid model E. Gomes & C. Blanco 10.2478/johh-2020-0043
- Uncertainties in projections of climate extremes indices in South America via Bayesian inference C. Gouveia et al. 10.1002/joc.7650
- Spatial and temporal rainfall variability and its controlling factors under an arid climate condition: case of Gabes Catchment, Southern Tunisia S. Jemai et al. 10.1007/s10668-021-01668-7
- Artificial neural networks in predicting of the gas molecular diffusion coefficient X. Wang et al. 10.1016/j.cherd.2023.10.035
- Spatial prediction of saline and sodic soils in rice‒shrimp farming land by using integrated artificial neural network/regression model and kriging Q. Dinh et al. 10.1080/03650340.2017.1352088
- Predicting water sorptivity coefficient in calcareous soils using a wavelet–neural network hybrid modeling approach A. Moosavi et al. 10.1007/s12665-021-09518-5
- Climate projections and downscaling techniques: a discussion for impact studies in urban systems M. Smid & A. Costa 10.1080/12265934.2017.1409132
- Seasonal probabilistic precipitation prediction in Comahue region (Argentina) using statistical techniques M. González et al. 10.1007/s00704-022-04324-w
- Multinomial logistic regression method for early detection of autism spectrum disorders D. Jayaprakash & C. Kanimozhiselvi 10.1016/j.measen.2024.101125
- Picea schrenkiana tree ring blue intensity reveal recent glacier mass loss in High Mountain Asia is unprecedented within the last four centuries W. Yue et al. 10.1016/j.gloplacha.2023.104210
- Enhancing short-term forecasting of daily precipitation using numerical weather prediction bias correcting with XGBoost in different regions of China J. Dong et al. 10.1016/j.engappai.2022.105579
- MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region E. Gomes et al. 10.1007/s10661-022-09939-0
- Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests . Balaji Prabhu B. V. & M. Dakshayini 10.4018/IJGHPC.2020100103
- Machine Learning for Hydropower Scheduling: State of the Art and Future Research Directions C. Bordin et al. 10.1016/j.procs.2020.09.190
- Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin R. Xu et al. 10.1155/2020/8680436
- Development of Precipitation Forecast Model Based on Artificial Intelligence and Subseasonal Clustering L. Parviz & K. Rasouli 10.1061/(ASCE)HE.1943-5584.0001862
- Predicting saturated and near-saturated hydraulic conductivity using artificial neural networks and multiple linear regression in calcareous soils H. Mozaffari et al. 10.1371/journal.pone.0296933
- Regional hydroclimatic projection using an coupled composite downscaling model with statistical bias corrector B. Kang & S. Moon 10.1007/s12205-017-1176-7
- Rainfall-related natural disasters in the Northeast of Brazil as a response to ocean-atmosphere interaction B. Silva et al. 10.1007/s00704-019-02930-9
2 citations as recorded by crossref.
- Bireysel Tüketici İhtiyaç Kredisi Talep Tahminlerinin Vektör Oto Regresyon ve Yapay Sinir Ağı Modelleri ile Karşılaştırmalı Analizi İ. ERTUĞRUL & A. ÖZÇİL 10.14784/marufacd.305559
- Medium-Term Rainfall Forecasts Using Artificial Neural Networks with Monte-Carlo Cross-Validation and Aggregation for the Han River Basin, Korea J. Lee et al. 10.3390/w12061743
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Latest update: 18 Apr 2024
Short summary
Statistical downscaling is widely used in large operational centers around the world, using exclusively linear relations (MLR); this study uses a statistical downscaling methodology using a nonlinear technique known as ANNs with CMIP5 project data. The artificial neural network can perform tasks that a linear program cannot. The main advantages of this are its temporal processing ability and its ability to incorporate several preceding predictor values as input without any additional effort.
Statistical downscaling is widely used in large operational centers around the world, using...
