Research on intelligent perception and optimization model of urban green space based on support vector regression
Bibliographic record
Abstract
The concept of urban green development promotes the development of intelligent technology as a new energy power, so that digital intelligent technology continues to enter into people’s vision, but also gradually accepted by the people. However, there is a lack of research on intelligent perception that focuses on residents’ attitudes, so this paper takes the theory of perceived value as the basis to analyze the influence path of intelligent perception of urban green space. Based on structural equation modeling, this paper explores the relationship between intelligent perception and residents’ attitudes in terms of perceived functional value, perceived emotional value, perceived social value, cognitive value and perceived risk. Then the intelligent perception prediction model for urban green space is constructed by using variational modal decomposition (VMD) combined with support vector regression (SVR), and the actual performance of this paper’s model is examined through experiments. This paper takes City Y as an example for prediction, and the results show that the intelligent perception of green space in City Y from 2023 to 2026 continues to show an upward trend. In addition, in order to prove the superiority of this paper’s model, its MAE, MAPE, RMSE and IA are compared with the prediction models of ARMA, BP, SVR and RF, respectively, and this paper’s model achieves the best results with the values of 4.2495, 15.8082, 3.5247 and 0.5225 for each index. In conclusion, the prediction model proposed in this paper has high accuracy in intelligent perception prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".