Technical System for Urban Stormwater Carrying Capacity Assessment and Optimization DOI Creative Commons

Kun Mao,

Junqi Li, Di Liu

и другие.

Buildings, Год журнала: 2025, Номер 15(11), С. 1889 - 1889

Опубликована: Май 30, 2025

The combined effects of rapid urbanization and climate change are increasingly exacerbating the risk urban flooding. This study develops a data-efficient framework for estimating city’s Urban Stormwater Carrying Capacity (USCC)—the maximum stormwater volume that can be safely infiltrated, stored, conveyed. couples three rainfall scenarios—frequent, heavy, extreme—with nine widely adopted drainage storage measures, ranging from green spaces permeable pavements to pipes underground emergency reservoirs, expresses USCC through streamlined water-balance equation. Applied 24 km2 Zhangmian River district in Weifang, China, yields capacities 4.84, 5.86, 9.80 × 106 m3 scenarios, respectively; reservoirs supply ≈ 40% extreme-event capacity. Sensitivity analysis shows increasing imperviousness coefficient 0.65 0.85 raises peak demand by 30.8%, whereas halving reservoir depth lowers total capacity 27.8%. Because method requires only depth, land-cover data, basic facility dimensions, it enables rapid, transparent scenario testing helps planners prioritize cost-effective upgrades. approach is transferable other cities extended incorporate water quality or digital-twin modules future research.

Язык: Английский

Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review DOI Creative Commons

Rameez Ahsen,

Pierpaolo Di Bitonto, Pierfrancesco Novielli

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4228 - 4228

Опубликована: Апрель 11, 2025

This systematic review explores the use of digital twins (DT) for sustainable agricultural water management. DTs simulate real-time environments, enabling precise resource allocation, predictive maintenance, and scenario planning. AI enhances DT performance through machine learning (ML) data-driven insights, optimizing usage. In this study, from an initial pool 48 papers retrieved well-known databases such as Scopus Web Science, etc., a rigorous eligibility criterion was applied, narrowing focus to 11 pertinent studies. highlights major disciplines where technology is being applied: hydroponics, aquaponics, vertical farming, irrigation. Additionally, literature identifies two key sub-applications within these disciplines: simulation prediction quality soil water. also types maturity levels concepts applications. Based on their current implementation, in agriculture can be categorized into functional types: monitoring DTs, which emphasize response environmental control, enable proactive irrigation management forecasting. techniques used framework were identified based These findings underscore transformative role that play enhancing efficiency sustainability Despite technological advancements, challenges remain, including data integration, scalability, cost barriers. Further studies should conducted explore issues practical farming environments.

Язык: Английский

Процитировано

0

Efficient urban flood control and drainage management framework based on digital twin technology and optimization scheduling algorithm DOI
Chenchen Fan, Jingming Hou, Xuan Li

и другие.

Water Research, Год журнала: 2025, Номер unknown, С. 123711 - 123711

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLM DOI Creative Commons
Prasant Mohanty, Sharmila Anand John Francis, Rabindra K. Barik

и другие.

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2839 - e2839

Опубликована: Апрель 29, 2025

Multi-disease conditions strain the body’s defenses, complicating recovery and increasing mortality risk. Therefore, effective concurrent prevention of multiple diseases is essential for mitigating complications improving overall well-being. Explainable artificial intelligence (XAI) with an advanced multimodal large language model (LLM) can create interactive system enabling general public to engage in natural without any specialized knowledge prerequisites. Counterfactual explanation, XAI method, offers valuable insights by suggesting adjustments patient features minimize disease risks. However, addressing simultaneously poses challenging barriers. This article proposes multi-disease that uses Google Gemini Pro, a LLM, non-dominated sorting genetic algorithm, namely NSGA-II, overcome such problems. recommends changes feature values concurrently risk as heart attacks diabetes. The facilitates personalized value selection, significantly reducing attack probabilities low possible. Such approach holds potential address unresolved issue preventing managing public.

Язык: Английский

Процитировано

0

Effectiveness of smart rain barrels for urban pluvial flood mitigation in densely built-up residential areas: a case study of Saitama City near Tokyo DOI Creative Commons
Jiajing Lin, Hiroyuki Taguchi,

Hitoshi Nakamura

и другие.

City and Built Environment, Год журнала: 2025, Номер 3(1)

Опубликована: Май 30, 2025

Abstract In recent years, rain barrels (RBs) have emerged as effective stormwater management tools for mitigating urban flooding. Our previous study in Saitama City revealed that traditional RBs performed poorly the densely populated residential areas compared to other low-impact development solutions. This investigated effectiveness of smart RB, comprising water pumps. These SRBs can be controlled online using switches rely on local weather forecasts, ensure are emptied before rainfall. We aimed assess how reduce pluvial flooding during continuous rainfall PySWMM model. varied drainage values simulations control activation switches, enabling us evaluate flood reduction. The results showed reduced total runoff volume by 2–10% RBs. Moreover, although reduction effect was found independent their size, increased proportionally with capacity.

Язык: Английский

Процитировано

0

Technical System for Urban Stormwater Carrying Capacity Assessment and Optimization DOI Creative Commons

Kun Mao,

Junqi Li, Di Liu

и другие.

Buildings, Год журнала: 2025, Номер 15(11), С. 1889 - 1889

Опубликована: Май 30, 2025

The combined effects of rapid urbanization and climate change are increasingly exacerbating the risk urban flooding. This study develops a data-efficient framework for estimating city’s Urban Stormwater Carrying Capacity (USCC)—the maximum stormwater volume that can be safely infiltrated, stored, conveyed. couples three rainfall scenarios—frequent, heavy, extreme—with nine widely adopted drainage storage measures, ranging from green spaces permeable pavements to pipes underground emergency reservoirs, expresses USCC through streamlined water-balance equation. Applied 24 km2 Zhangmian River district in Weifang, China, yields capacities 4.84, 5.86, 9.80 × 106 m3 scenarios, respectively; reservoirs supply ≈ 40% extreme-event capacity. Sensitivity analysis shows increasing imperviousness coefficient 0.65 0.85 raises peak demand by 30.8%, whereas halving reservoir depth lowers total capacity 27.8%. Because method requires only depth, land-cover data, basic facility dimensions, it enables rapid, transparent scenario testing helps planners prioritize cost-effective upgrades. approach is transferable other cities extended incorporate water quality or digital-twin modules future research.

Язык: Английский

Процитировано

0