首页|Findings on Machine Learning Reported by Investigators at Cerema (Supporting the Design of On-site Infiltration Systems: From a Hydrological Model To a Web App To Meet Pluriannual Stormwater Volume Reduction Targets)
Findings on Machine Learning Reported by Investigators at Cerema (Supporting the Design of On-site Infiltration Systems: From a Hydrological Model To a Web App To Meet Pluriannual Stormwater Volume Reduction Targets)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting originating from Trappes, France, by New sRx correspondents, research stated, “Infiltration-based sustainable urban drain age systems (i-SUDS) often turn out to be simple and effective solutions for on- site runoff and pollution control. Their ability to limit the discharge to sewer networks or receiving waters can be broadly assessed in terms of (pluri)annual stormwater volume reduction.” Funders for this research include French Ministry of the Environment, Seine Norm andie Water Agency, OPUR partners (Seine-Normandy Water Agency, Val-de-Marne Dep artmental Council, Seine-Saint-Denis Departmental Council), Hauts-de-Seine Depar tmental Council, Seine-et-Marne Departmental Council.
TrappesFranceEuropeCyborgsEmergi ng TechnologiesMachine LearningCerema
NETL
NSTL
