Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification

By Ashish Sharma. 2000

298 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/92bea8dea058de57c3aed8258d9086ccd06ec7f3

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In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Review of Input Variable Selection Methods for Artificial Neural Networks (2011, methodology): “In this case, the termination criterion is based upon the distribution of the error in PMI estimation, which is numerically approximated by a bootstrap approach (Sharma, 2000).”
Tree‐based iterative input variable selection for hydrological modeling (2013, methodology): “Comparison Against PMI-Based Input Selection [42] To further validate the results obtained with the IIS algorithm, Experiments 1 and 2 are solved by means of the PMIS algorithm [Sharma, 2000; May et al., 2008], which found successful application in different hydrological problems.”
A nonparametric approach for representing interannual dependence in monthly streamflow sequences (2002, methodology): “…in case of a real application using an order selection scheme such as the Akaike information criterion (AIC) [Akaike, 1974] or generalized cross validation (GCV) [Craven and Wahba, 1979] or a nonparametric measure of partial dependence, the partial mutual information (PMI) criterion [Sharma, 2000].”
Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling (2016, methodology): “For instance, the intermediate regression models have been specified in the literature through multivariate kernel density estimates [Sharma, 2000a,b; Sharma et al., 2000; May et al., 2008a,b; He et al., 2011] and general regression neural networks (GRNN) [Bowden et al., 2005a,b; Chen et al.,…”
An information theoretic alternative to model a natural system using observational information alone (2014, methodology): “…al., 1995; D’haeseleer et al., 2000; Carsten et al., 2004; Hahn et al., 2005], physics [Fraser and Swinney, 1986], image recognition [Th enevaz and Unser, 2000], speech recognition [Ellis and Bilmes, 2000], language processing [Brown et al., 1992], and hydrology [Sharma, 2000a; Hoyos et al., 2006].”
Long‐lead probabilistic forecasting of streamflow using ocean‐atmospheric and hydrological predictors (2006, methodology): “Statistical approaches that relate streamflow to large-scale ocean-atmosphere state variables provide a basis for useful seasonal to interannual forecasts [ Sharma, 2000a ].”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, methodology): “Examples of this approach in hydrological research include the kernel-based estimator [Sharma, 2000], bin counting method [Ruddell and Kumar, 2009], and the average-shifted histogram method [Fernando et al., 2009].”
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (2014, methodology): “Model free techniques include analytical methods based on correlation (Li et al., 2008) and mutual information (MI) (Sharma, 2000), and ad-hoc methods based on available data and/ or domain knowledge.”
Input determination for neural network models in water resources applications. Part 1—background and methodology (2005, methodology): “The first three models used to generate the synthetic data are time series models used by Sharma (2000). The fourth model is a nonlinear system.”