| dc.contributor.author | Chen, Shiguang | |
| dc.contributor.author | Sun, Hongwei | |
| dc.contributor.author | Chen, Xuebin | |
| dc.date.accessioned | 2026-08-20T11:06:48Z | |
| dc.date.available | 2026-08-20T11:06:48Z | |
| dc.date.copyright | 2026/04/06 | |
| dc.date.issued | 2026 | |
| dc.identifier.other | JHYEFF.HEENG-6587.pdf | |
| dc.identifier.uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4311709 | |
| dc.description.abstract | AbstractRainwater 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.publisher | American Society of Civil Engineers | |
| dc.title | Predicting the Optimal Tank Volume of a Rainwater Harvesting System: A Machine Learning Approach Based on Building Characteristics | |
| dc.type | Journal Article | |
| dc.journal.volume | 31 | |
| dc.journal.issue | 3 | |
| dc.journal.title | Journal of Hydrologic Engineering | |
| dc.identifier.doi | 10.1061/JHYEFF.HEENG-6587 | |
| dc.journal.fristpage | 04026008-1 | |
| dc.journal.lastpage | 04026008-14 | |
| dc.page | 14 | |
| dc.tree | Journal of Hydrologic Engineering:;2026:;Volume ( 031 ):;issue: 003 | |
| dc.contenttype | Fulltext | |