| scholarly article | Q13442814 |
| P356 | DOI | 10.1002/HYP.9300 |
| P2093 | author name string | Inmaculada Pulido-Calvo | |
| João Filipe Santos | |||
| Maria Manuela Portela | |||
| P2860 | cites work | A Mathematical Theory of Communication | Q724029 |
| River flow forecasting through conceptual models part I — A discussion of principles | Q29400253 | ||
| Learning representations by back-propagating errors | Q29469983 | ||
| A method to determine the required number of neural-network training repetitions | Q47932109 | ||
| Clustering files of chemical structures using the fuzzy k-means clustering method | Q48609833 | ||
| Radial Basis Function Network Configuration Using Mutual Information and the Orthogonal Least Squares Algorithm. | Q51649052 | ||
| Selecting inputs for modeling using normalized higher order statistics and independent component analysis. | Q51894383 | ||
| Application of neural networks and sensitivity analysis to improved prediction of trauma survival. | Q52081509 | ||
| Artificial Neural Networks in Hydrology. I: Preliminary Concepts | Q56058047 | ||
| Artificial Neural Networks in Hydrology. II: Hydrologic Applications | Q56058048 | ||
| Principles of geostatistics | Q57254490 | ||
| Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation | Q57258580 | ||
| Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach | Q57606642 | ||
| Non-linear variable selection for artificial neural networks using partial mutual information | Q57606663 | ||
| Input determination for neural network models in water resources applications. Part 1—background and methodology | Q57606713 | ||
| Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications | Q57606782 | ||
| Artificial Neural Network Modeling of the Rainfall-Runoff Process | Q58393582 | ||
| Regional Frequency Analysis of Droughts in Portugal | Q58891911 | ||
| Anchovy (Engraulis ringens) and sardine (Sardinops sagax) abundance forecast off northern Chile: A multivariate ecosystemic neural network approach | Q58891928 | ||
| Spatial and temporal variability of droughts in Portugal | Q58891934 | ||
| Pacific sardine (Sardinops sagax, Jenyns 1842) landings prediction. A neural network ecosystemic approach | Q58891947 | ||
| Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds | Q58891964 | ||
| Monthly catch forecasting of anchovy Engraulis ringens in the north area of Chile: Non-linear univariate approach | Q58891973 | ||
| A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters | Q59119268 | ||
| Algorithm AS 136: A K-Means Clustering Algorithm | Q105584859 | ||
| P433 | issue | 10 | |
| P921 | main subject | dimensionality reduction | Q16000077 |
| drought | Q43059 | ||
| P304 | page(s) | 1399-1410 | |
| P577 | publication date | 2012-04-17 | |
| P1433 | published in | Hydrological Processes | Q15763791 |
| P1476 | title | Dimensionality reduction in drought modelling | |
| P478 | volume | 27 |
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