A Systematic Review of Artificial Intelligence in Geographic Information Systems DOI
Son Nguyen-Kim, Vinh T. Nguyen, Duc-Binh Nguyen

et al.

Lecture notes in networks and systems, Journal Year: 2023, Volume and Issue: unknown, P. 20 - 31

Published: Jan. 1, 2023

Language: Английский

The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management DOI Creative Commons
Hesam Kamyab, Tayebeh Khademi, Shreeshivadasan Chelliapan

et al.

Results in Engineering, Journal Year: 2023, Volume and Issue: 20, P. 101566 - 101566

Published: Nov. 3, 2023

The effective management of water resources is essential to environmental stewardship and sustainable development. Traditional approaches resource (WRM) struggle with real-time data acquisition, analysis, intelligent decision-making. To address these challenges, innovative solutions are required. Artificial Intelligence (AI) Big Data Analytics (BDA) at the forefront have potential revolutionize way managed. This paper reviews current applications AI BDA in WRM, highlighting their capacity overcome existing limitations. It includes investigation technologies, such as machine learning deep learning, diverse quality monitoring, allocation, demand forecasting. In addition, review explores role resources, elaborating on various sources that can be used, remote sensing, IoT devices, social media. conclusion, study synthesizes key insights outlines prospective directions for leveraging optimal allocation.

Language: Английский

Citations

122

Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities DOI
Abbas Sharifi, Ali Tarlani Beris,

Amir Sharifzadeh Javidi

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 61, P. 102485 - 102485

Published: March 26, 2024

Language: Английский

Citations

26

Bibliometric analysis of global performance and trends of research on combined sewer overflows (CSOs) from 1990 to 2022 DOI Creative Commons

Qingbang Yang,

Chen Shen, Chunhua Li

et al.

Water Science & Technology, Journal Year: 2024, Volume and Issue: 89(6), P. 1554 - 1569

Published: March 8, 2024

Abstract Combined sewer overflows (CSOs) are one of the main sources pollution in urban water systems and significantly impede restoration body functionalities within rivers lakes. To understand research frontier trends CSOs comprehensively systematically, a visual statistical analysis literature related to Web Science core database from 1990 2022 was conducted using bibliometric method HistCite Pro VOSviewer. The results reveal total 1,209 pertinent publications 2022, quantity CSOs-related indicated an increasing trend. Investigations distribution fate typical pollutants their ecological effects on receiving waters studies control technologies (source reduction, process control, end-of-pipe treatment) current focus research. based source reduction monitoring emerging contaminants at forefront scientific investigations CSOs. This study systematically summarized topics future directions CSOs, thus providing reference for environment management

Language: Английский

Citations

6

A state-of-the-art review for the prediction of overflow in urban sewer systems DOI

Shihui Ma,

Tarek Zayed, Jiduo Xing

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 434, P. 139923 - 139923

Published: Nov. 28, 2023

Language: Английский

Citations

16

Optimization of LID Strategies for Urban CSO Reduction and Cost Efficiency: A Beijing Case Study DOI Open Access
Hao Wang,

Pengfei Zeng,

Zilong Liu

et al.

Water, Journal Year: 2024, Volume and Issue: 16(7), P. 965 - 965

Published: March 27, 2024

Combined sewer overflow (CSO) can lead to serious urban water environment pollution and health risks residents. Low Impact Development (LID) facilities are one of the important measures alleviate CSO have been widely applied. The rational selection LID facility types, locations, scales is most task, which effectively improve resource utilization efficiency. Based on NSGA-II multi-objective optimization algorithm coupled with SWMM network hydraulic model, this study takes combined overflows construction cost as objectives optimizes types layout in area, including eight different return periods. By using Pareto frontier visualizing results effects rainfall periods control investment schemes compared. show following: (1) model demonstrate relationship between volume under through frontier, showing three trends, indicating that nonlinear; (2) increase intensity, higher requirements proposed for meet targets, leading a decrease number solution sets. Under larger intensities, it difficult achieve same effect by increasing scale construction. Therefore, considering constraining RMB 5.3 5.38 million helpful determine suitable solution; (3) optimal periods, 87.3% locations where deployed similar scales. these relatively large proportion deployment, be determined special attention should paid spatial positions planning This provides valuable insights solving problems optimizing drainage management guidance future decision-making processes.

Language: Английский

Citations

5

Advances and Challenges of Digital Twin Technology in Urban Drainage Systems DOI

水金 葛

Sustainable Development, Journal Year: 2025, Volume and Issue: 15(01), P. 46 - 54

Published: Jan. 1, 2025

Language: Английский

Citations

0

Comparative analysis to characterize the sewage water using novel sensor based water quality analyser against laboratory analysis DOI

Yarrabathina Laxmi Supriya,

A. Velumani

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3252, P. 020193 - 020193

Published: Jan. 1, 2025

Language: Английский

Citations

0

Ecological impacts of combined sewer overflows on receiving waters DOI Creative Commons
Teressa Negassa Muleta, Marcell Knolmár

Discover Water, Journal Year: 2025, Volume and Issue: 5(1)

Published: March 17, 2025

Language: Английский

Citations

0

Digital Twin-Enabled Regional Food Supply Chain: A Review and Research Agenda DOI Creative Commons
José Monteiro, João Barata

Journal of Industrial Information Integration, Journal Year: 2025, Volume and Issue: unknown, P. 100851 - 100851

Published: April 1, 2025

Language: Английский

Citations

0

Research on interpretable graph neural network agent model for siltation diagnosis in Urban-Scale sewer systems DOI
Junhao Wu, Ling Ma, Xi Chen

et al.

Tunnelling and Underground Space Technology, Journal Year: 2025, Volume and Issue: 162, P. 106666 - 106666

Published: April 16, 2025

Language: Английский

Citations

0