Publications -- Query Results

<< 1 >>

Citations

Chang, N. - B., Yang, Y. J., Imen, S., & Mullon, L. (2017). Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models. Journal of Hydrology, 548, 305–321. Keywords: Climate change; Precipitation; Sea surface temperature; Wavelet; Teleconnection patterns; Artificial neural networks; Forecasting http://floridaclimateinstitute.org/refbase/show.php?record=1570
Teegavarapu, R. S. V., Aly, A., Pathak, C. S., Ahlquist, J., Fuelberg, H., & Hood, J. (2018). Infilling missing precipitation records using variants of spatial interpolation and data-driven methods: use of optimal weighting parameters and nearest neighbour-based corrections. Int. J. Climatol, 38(2), 776–793. Keywords: spatial interpolation; support vector machine; single best estimators; single best classifier; artificial neural networks; linear weight optimization; South Florida; missing precipitation http://floridaclimateinstitute.org/refbase/show.php?record=1886

The Florida Climate Institute (FCI) is a multi-disciplinary network of national and international research and public organizations, scientists, and individuals concerned with achieving a better understanding of climate variability and change.