Articles | Volume 23, issue 1
Nonlin. Processes Geophys., 23, 13–20, 2016
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.
Nonlin. Processes Geophys., 23, 13–20, 2016
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 et al.
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Cited
12 citations as recorded by crossref.
- Daily rainfall estimates considering seasonality from a MODWT-ANN hybrid model E. Gomes & C. Blanco 10.2478/johh-2020-0043
- 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
- 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
- Development of Precipitation Forecast Model Based on Artificial Intelligence and Subseasonal Clustering L. Parviz & K. Rasouli 10.1061/(ASCE)HE.1943-5584.0001862
- 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
10 citations as recorded by crossref.
- Daily rainfall estimates considering seasonality from a MODWT-ANN hybrid model E. Gomes & C. Blanco 10.2478/johh-2020-0043
- 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
- 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
- Development of Precipitation Forecast Model Based on Artificial Intelligence and Subseasonal Clustering L. Parviz & K. Rasouli 10.1061/(ASCE)HE.1943-5584.0001862
- 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: 10 Apr 2021
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...
