Research on Landscape Design Optimization and Spatial Layout Planning Method Based on AI Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditional landscape design methods have low efficiency, poor subjectivity and insufficient goal optimization.This paper proposes a landscape design optimization and spatial layout method based on artificial intelligence (AI) algorithms to achieve scientific and efficient landscape design through the combination of collected information data and algorithms.The optimization design of landscape facility paths and spatial dimensions is carried out by adopting a heuristic polygonal layout algorithm, establishing a data model based on the database and scene templates, and combining the landscapes in the polygonal space after landscape matching.The optimal sequence of the landscape is obtained by using the scoring function, and then combined with the particle swarm algorithm to realize the optimization of the landscape layout.The Hypervolume index is stable to about 0.815 in 30 generations, which has a good quality of Pareto optimal solution set.In this paper, the algorithm formulates three groups of landscape design optimization and spatial layout planning schemes for different situations, making full use of the land that is utilized for a certain place.The implementation of the sustainable development scenarios improves the local environmental and social benefits significantly, and the average annual growth rate of employment in related industries reaches 3.16%.Satisfaction survey results show that local residents are most satisfied with the green environment and cultural atmosphere after the implementation of the program, respectively 80.03, 79.35, through the smart management to improve the local environmental quality and cultural atmosphere.
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it