Author(s)

XiaoYao Liu

Affiliation(s)

Shandong Normal University Academy of Fine Arts, Shandong Normal University, Jinan 250014, Shandong, China.

Corresponding Author

XiaoYao Liu

ABSTRACT

Urban waterfront spaces, as core carriers integrating the natural and human environment, possess both ecological regulation and public activity functions. Their quality directly impacts user satisfaction. However, current construction suffers from problems such as incoordination with the urban environment and insufficient ecological protection. Traditional satisfaction evaluation methods based on questionnaires and expert ratings are highly subjective and inefficient. This study aims to construct an efficient and accurate prediction model for urban waterfront space landscape satisfaction, providing technical support for spatial planning optimization. Through a multi-source data fusion strategy, landscape images, environmental parameters, user questionnaires, and behavioral data were collected to construct a comprehensive dataset. A prediction model was designed based on a convolutional neural network (CNN), optimizing key parameters such as kernel size and learning rate. Performance was compared with support vector machine (SVM) and random forest (RF) models. The results show that the constructed CNN model achieves a prediction accuracy of 87.3%, a coefficient of determination R2 of 0.891, and a root mean square error (RMSE) of 0.324, significantly outperforming traditional machine learning models. This study introduces deep learning technology into the field of urban waterfront space evaluation, improving the efficiency and objectivity of satisfaction assessment and providing new technical paths and decision-making basis for the sustainable development of urban waterfront spaces and urban renewal.

KEYWORDS

Waterfront space; Satisfaction prediction; Convolutional neural network; Urban planning; Ecological environment

CITE THIS PAPER

XiaoYao Liu. Satisfaction prediction of urban waterfront space landscapes using CNNS. Trends in Social Sciences and Humanities Research. 2026, 4(1): 59-66. DOI: https://doi.org/10.61784/tsshr3212.

REFERENCES

[1] Yang Yu, Lin Chengxiang. Application of SD analysis method in the evaluation and improvement of waterfront space vitality. Sichuan Cement, 2024.

[2]  Shi Tianhao. Research on the role and importance of urban waterfront spaces. Beauty and Times (Urban Edition), 2024.

[3] Liu Si, Ye Ziyun, Fan Lixue, et al. Research on satisfaction evaluation of urban waterfront public spaces based on AHP-IPA method. Urban Architecture, 2024.

[4] Wang Leru, Zhang Haozhou. Landscape design of Huai'an waterfront space from the perspective of cultural tourism. Modern Horticulture, 2024.

[5]  Dang Zhijuan. Research on the optimization of public facility design in urban waterfront landscape space. Packaging Engineering, 2024.

[6]  Li Jinrong, Hu Die. Exploration of ecological system design of urban waterfront space. Modern Horticulture, 2024.

[7] Gan He, Qin Yu. Exploration of waterfront landscape covered walkway design - taking the design of covered walkway and pavilion on the Hongshui River in Dahua County as an example. Stone, 2024.

[8]  Wu Yujing, Zhang Xiaoyu, Shen Jie. Post-use evaluation of the central vitality section of the Huangpu River in Shanghai based on public review data. Residential Technology, 2024.

[9] Fu Ze, Tang Li. Research on the evaluation of the Guitang River waterfront landscape based on AHP-SD method. Forestry Science and Technology Information, 2024.

[10] Cheng Shi, Lang Leijie, Yang Xiangyu. Construction of urban waterfront interface morphology evaluation and analysis model based on dynamic visual attention of the crowd - taking the Nanjing Youth Olympic Area as an example. Landscape Architecture, 2024.

[11] Li Jiaqi. Design strategy of Fuzhou Luozhou Town waterfront wetland park under the guidance of minimum intervention. Architecture and Culture, 2024.