Plant Landscape Configuration Method of Regional Characteristic Rainwater Garden Based on Deep Learning

With the acceleration of urbanization and the continuous expansion and construction of cities, more and more cities in China are facing increasingly serious urban rainwater problems. As a rainwater management measure under the low impact development system, rainwater garden can manage and utilize ra...

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Bibliographic Details
Published in:Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Main Author: He Q.; Ng J.L.; Noh N.I.F.M.
Format: Conference paper
Language:English
Published: Springer Science and Business Media Deutschland GmbH 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144514493&doi=10.1007%2f978-3-031-18123-8_27&partnerID=40&md5=52335354ffedb25d71a6386c7e352b43
Description
Summary:With the acceleration of urbanization and the continuous expansion and construction of cities, more and more cities in China are facing increasingly serious urban rainwater problems. As a rainwater management measure under the low impact development system, rainwater garden can manage and utilize rainwater resources. Therefore, a plant landscape configuration method of regional characteristic rainwater garden based on in-depth learning is proposed. By analyzing the types and characteristics of rainwater landscape facilities; Constructed rainwater garden runoff management system; Design the plant configuration scheme of wet area, semi-humid area, arid area and semi-arid area of rainwater garden; The plant landscape characteristic parameters are calculated based on the deep learning algorithm to complete the plant landscape configuration. Experiments show that the waterlogging tolerance index D of this rainwater garden landscape configuration method for different kinds of plant landscape is higher than that of the traditional configuration method, which can manage rainwater and improve the ecological environment at the same time. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
ISSN:18678211
DOI:10.1007/978-3-031-18123-8_27