Frequency Analysis of Droughts Using Stochastic and Soft Computing Techniques
Bibliographic record
Abstract
In the Canadian Prairies recurring droughts are one of the realities which can \nhave significant economical, environmental, and social impacts. For example, \ndroughts in 1997 and 2001 cost over $100 million on different sectors. Drought frequency \nanalysis is a technique for analyzing how frequently a drought event of a given \nmagnitude may be expected to occur. In this study the state of the science related \nto frequency analysis of droughts is reviewed and studied. The main contributions \nof this thesis include development of a model in Matlab which uses the qualities of \nFuzzy C-Means (FCMs) clustering and corrects the formed regions to meet the criteria \nof effective hydrological regions. In FCM each site has a degree of membership in \neach of the clusters. The algorithm developed is flexible to get number of regions and \nreturn period as inputs and show the final corrected clusters as output for most case \nscenarios. While drought is considered a bivariate phenomena with two statistical \nvariables of duration and severity to be analyzed simultaneously, an important step \nin this study is increasing the complexity of the initial model in Matlab to correct \nregions based on L-comoments statistics (as apposed to L-moments). Implementing \na reasonably straightforward approach for bivariate drought frequency analysis using \nbivariate L-comoments and copula is another contribution of this study. Quantile estimation at ungauged sites for return periods of interest is studied by introducing two \nnew classes of neural network and machine learning: Radial Basis Function (RBF) \nand Support Vector Machine Regression (SVM-R). These two techniques are selected \nbased on their good reviews in literature in function estimation and nonparametric \nregression. The functionalities of RBF and SVM-R are compared with traditional \nnonlinear regression (NLR) method. As well, a nonlinear regression with regionalization \nmethod in which catchments are first regionalized using FCMs is applied and \nits results are compared with the other three models. Drought data from 36 natural \ncatchments in the Canadian Prairies are used in this study. This study provides a \nmethodology for bivariate drought frequency analysis that can be practiced in any \npart of the world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".