Sustainable Production and Consumption, Journal Year: 2023, Volume and Issue: 39, P. 109 - 122
Published: May 16, 2023
Language: Английский
Heliyon, Journal Year: 2024, Volume and Issue: 10(14), P. e34478 - e34478
Published: July 1, 2024
The environmental impact of the construction industry is very heavy. Therefore, global community further developing green building assessment tools in order to enhance their efficiency matching sustainability goals and being more environmentally friendly. An analysis approach by means Multi-Criteria Decision Making (MCDM) was carried out examine degree response different utilized Europe, namely (Innovazione e Trasparenza degli Appalti la Compatibilità Ambientale ITACA, Deutsches Guetsiegel Nachhaltiges Bauen (DGNB), Haute Qualité Environnementale (HQE) Sustainable Building Tool (SBTool), eight criteria European Green Deal (EGD), a roadmap elaborated Commission deployment region. first phase consisted Boolean MCDM aiming define which criterion EGD each indicator checklists linked. These data obtained were later examined Fuzzy Logic obtain comparable results showing how much tool helps following towards sustainability. This work intends compare most used Europe for evaluation while based on particular specified reference not only sustainable general. also shows combining two MCDCM techniques better analyzing output. result this study that DGNB effective method connecting all balanced manner. HQE demonstrated strong ability effectively integrate objectives EGD, except energy aspect. For ITACA tool, it closely aligned its although had an absent focus smart shift mobility. SBTool average performance when compared other protocols. expected since basis DGNB, constructed.
Language: Английский
Citations
2
Agricultural Water Management, Journal Year: 2024, Volume and Issue: 305, P. 109118 - 109118
Published: Oct. 30, 2024
Language: Английский
Citations
2
Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 25, 2024
Abstract Managed aquifer recharge (MAR) replenishes groundwater by artificially entering water into subsurface aquifers. This technology improves storage, reduces over-extraction, and ensures security in water-scarce or variable environments. MAR systems are complex, encompassing various components such as soil, meteorological factors, management (GWM), receiving bodies. Over the past decade, utilization of machine learning (ML) methodologies for modeling prediction has increased significantly. review evaluates all supervised, semi-supervised, unsupervised, ensemble ML models employed to predict factors parameters, rendering it most comprehensive contemporary on this subject. study presents a concise integrated overview MAR’s effective approaches, focusing design, suitability quality (WQ) applications, GWM. The paper examines performance measures, input specifications, variety functions GWM, highlights prospects. It also offers suggestions utilizing MAR, addressing issues related physical aspects, technical advancements, case studies. Additionally, previous research ML-based data-driven soft sensing techniques is critically evaluated. concludes that integrating holds significant promise optimizing WQ enhancing efficiency replenishment strategies.
Language: Английский
Citations
2
Environmental Development, Journal Year: 2024, Volume and Issue: unknown, P. 101122 - 101122
Published: Nov. 1, 2024
Language: Английский
Citations
2
Sustainable Production and Consumption, Journal Year: 2023, Volume and Issue: 39, P. 109 - 122
Published: May 16, 2023
Language: Английский
Citations
6