Show simple item record

dc.contributor.authorChen, Shiguang
dc.contributor.authorSun, Hongwei
dc.contributor.authorChen, Xuebin
dc.date.accessioned2026-08-20T11:06:48Z
dc.date.available2026-08-20T11:06:48Z
dc.date.copyright2026/04/06
dc.date.issued2026
dc.identifier.otherJHYEFF.HEENG-6587.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311709
dc.description.abstractAbstractRainwater harvesting (RWH) is a promising strategy for addressing water scarcity, and the optimal design of storage tanks is vital for their efficiency. This study develops a predictive model using machine learning algorithms, specifically Random ...Schematic diagram of the study: the left side illustrates building characteristics that may influence the design of the RWH system tank volume, while the right side presents the machine learning workflow for predicting the optimal tank size.Schematic ...
dc.publisherAmerican Society of Civil Engineers
dc.titlePredicting the Optimal Tank Volume of a Rainwater Harvesting System: A Machine Learning Approach Based on Building Characteristics
dc.typeJournal Article
dc.journal.volume31
dc.journal.issue3
dc.journal.titleJournal of Hydrologic Engineering
dc.identifier.doi10.1061/JHYEFF.HEENG-6587
dc.journal.fristpage04026008-1
dc.journal.lastpage04026008-14
dc.page14
dc.treeJournal of Hydrologic Engineering:;2026:;Volume ( 031 ):;issue: 003
dc.contenttypeFulltext


Files in this item

Thumbnail
This item appears in the following Collection(s)

Show simple item record