Spiking modular neural networks: A neural network modeling approach for hydrological processes
Bibliographic record
Abstract
Artificial Neural Networks (ANNs) have been widely used for modeling hydrological processes that are embedded with high nonlinearity in both spatial and temporal scales. The input‐output functional relationship does not remain the same over the entire modeling domain, varying at different spatial and temporal scales. In this study, a novel neural network model called the spiking modular neural networks (SMNNs) is proposed. An SMNN consists of an input layer, a spiking layer, and an associator neural network layer. The modular nature of the SMNN helps in finding domain‐dependent relationships. The performance of the model is evaluated using two distinct case studies. The first case study is that of streamflow modeling, and the second case study involves modeling of eddy covariance‐measured evapotranspiration. Two variants of SMNNs were analyzed in this study. The first variant employs a competitive layer as the spiking layer, and the second variant employs a self‐organizing map as the spiking layer. The performance of SMNNs is compared to that of a regular feed forward neural network (FFNN) model. Results from the study demonstrate that SMNNs performed better than FFNNs for both the case studies. Results from partitioning analysis reveal that, compared to FFNNs, SMNNs are effective in capturing the dynamics of high flows. In modeling evapotranspiration, it is found that net radiation and ground temperature alone can be used to model the evaporation flux effectively. The SMNNs are shown to be effective in discretizing the complex mapping space into simpler domains that can be learned with relative ease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".