Data-Driven Approach for Optimising Plant Species Selection and Planting Design on Outdoor Modular Green Wall with Aesthetic, Maintenance, and Water-Saving Goals

Caroline M. Y. Law, Hoi Yi Law, Chi Ho Li, Chung Wai Leung, Min Pan (), Si Chen (), Kenrick C. K. Ho and Yik Tung Sham
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Caroline M. Y. Law: Nature in situ Limited, Hong Kong SAR, China
Hoi Yi Law: Department of Construction, Environment and Engineering, Technological and Higher Education Institute of Hong Kong, Hong Kong SAR, China
Chi Ho Li: Department of Construction, Environment and Engineering, Technological and Higher Education Institute of Hong Kong, Hong Kong SAR, China
Chung Wai Leung: Department of Applied Science, School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Kowloon, Hong Kong SAR, China
Min Pan: Department of Applied Science, School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Kowloon, Hong Kong SAR, China
Si Chen: Department of Land Surveying and Geo-Informatics, Faculty of Construction and Environment, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
Kenrick C. K. Ho: Department of Applied Science, School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Kowloon, Hong Kong SAR, China
Yik Tung Sham: Department of Applied Science, School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Kowloon, Hong Kong SAR, China

Sustainability, 2025, vol. 17, issue 8, 1-23

Abstract: Modular green wall, or living wall (LW) system, has evolved worldwide over the past decades as a popular green building feature and a nature-based solution. Differential climatic conditions across the globe make the standardisation of practices inapplicable to local scenarios. LW projects with differing goals and preferences like aesthetic (such as plant healthiness), water-saving, and minimal plant growth require optimal combinations of plant species to achieve single or multiple goals. This exploratory study aimed to deploy empirical field LW data to optimise analytical models to support plant species selection and LW design. Plant growth performance and water demand data of 29 commonly used plant species in outdoor modular LW systems without irrigation were collected in subtropical Hong Kong for 3 weeks. The 29 species tested were grouped into five plant forms: herbaceous perennials (16 spp), succulents (2 spp), ferns (2 spp), shrubs (7 spp), and trees (2 spp). Plant species-specific plant height, LAI, plant health rating, and water absorption amount were recorded every 6 days, together with photo records. Total water demand varied widely among plant species, ranging from 52.5 to 342.5 mL in 3 weeks (equivalent to 2.5 to 16.3 mL per day). The random forest algorithm proved that the water demand of the species was a dominant predictor of plant health tendency, among other parameters. Hierarchical clustering grouped plant species with similar water demand and health rating tendencies into four groups. The health rating threshold approach identified the top-performing species that displayed a healthy appearance as a basic prerequisite, coupled with one or two optional objectives: (1) water-saving and (2) slow-growing. The comparison among the plant selection scenarios based on projected LW performance (water demand, plant health, and growth) provided sound evidence for the optimisation of LW design for sustainability. LW projects with multiple objectives inherited a multitude of multi-scalar properties; thus, the simulation of LW performance in this study demonstrated a novel data-driven approach to optimise plant species selection and planting design with minimal resource input.

Keywords: modular green wall; water demand; plant performance; plant species selection; data-driven approach (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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