Geospatial Clustering in Smart City Resource Management: An Initial Step in the Optimisation of Complex Technical Supply Systems; Smart Cities; Vol. 8, iss. 1
| Parent link: | Smart Cities.— .— Basel: MDPI AG Vol. 8, iss. 1.— 2025.— Article number 14, 17 p. |
|---|---|
| その他の著者: | , , , , , , |
| 要約: | Title screen For large cities with developing infrastructures, optimising water supply systems plays a crucial role. However, without a clear understanding of the network structure and water consumption patterns, addressing these challenges becomes significantly more complex. This paper proposes a methodology for geospatial data analysis aimed at solving two key tasks. The first is the delineation of service zones for infrastructure objects to enhance system manageability. The second involves the development of an approach for the optimal placement of devices to collect and transmit hydraulic network parameters, ensuring their alignment with both water supply sources and serviced areas. The study focuses on data from the water supply network of a city with a population exceeding half a million people, where hierarchical clustering using Ward’s method was applied to analyse territorial distribution. Four territorial clusters were identified, each characterised by unique attributes reflecting consumer concentration and water consumption volumes. The cluster boundaries were compared with the existing service scheme of the system, confirming their alignment with real infrastructure. The quality of clustering was further evaluated using the silhouette coefficient, which validated the high accuracy and reliability of the chosen approach. The paper demonstrates the effectiveness of cluster boundary visualisation for assessing the uniform distribution of pressure sensors within the urban water supply network. The results of the study show that integrating geographic data with water consumption information not only facilitates effective infrastructure planning and resource allocation but also lays the foundation for the digitalization of the hydraulic network, a critical component of sustainable development in modern smart cities Текстовый файл |
| 言語: | 英語 |
| 出版事項: |
2025
|
| 主題: | |
| オンライン･アクセス: | http://earchive.tpu.ru/handle/11683/132431 https://doi.org/10.3390/smartcities8010014 |
| フォーマット: | 電子媒体 図書の章 |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680060 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 680060 | ||
| 005 | 20250912112152.0 | ||
| 090 | |a 680060 | ||
| 100 | |a 20250506d2025 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 102 | |a CH | ||
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i |b e | |
| 182 | 0 | |a b | |
| 183 | 0 | |a cr |2 RDAcarrier | |
| 200 | 1 | |a Geospatial Clustering in Smart City Resource Management: An Initial Step in the Optimisation of Complex Technical Supply Systems |f Aliaksey A. Kapanski, Roman V. Klyuev, Aleksandr E. Boltrushevich [et al.] | |
| 203 | |a Текст |b визуальный |c электронный | ||
| 283 | |a online_resource |2 RDAcarrier | ||
| 300 | |a Title screen | ||
| 320 | |a References: 34 tit | ||
| 330 | |a For large cities with developing infrastructures, optimising water supply systems plays a crucial role. However, without a clear understanding of the network structure and water consumption patterns, addressing these challenges becomes significantly more complex. This paper proposes a methodology for geospatial data analysis aimed at solving two key tasks. The first is the delineation of service zones for infrastructure objects to enhance system manageability. The second involves the development of an approach for the optimal placement of devices to collect and transmit hydraulic network parameters, ensuring their alignment with both water supply sources and serviced areas. The study focuses on data from the water supply network of a city with a population exceeding half a million people, where hierarchical clustering using Ward’s method was applied to analyse territorial distribution. Four territorial clusters were identified, each characterised by unique attributes reflecting consumer concentration and water consumption volumes. The cluster boundaries were compared with the existing service scheme of the system, confirming their alignment with real infrastructure. The quality of clustering was further evaluated using the silhouette coefficient, which validated the high accuracy and reliability of the chosen approach. The paper demonstrates the effectiveness of cluster boundary visualisation for assessing the uniform distribution of pressure sensors within the urban water supply network. The results of the study show that integrating geographic data with water consumption information not only facilitates effective infrastructure planning and resource allocation but also lays the foundation for the digitalization of the hydraulic network, a critical component of sustainable development in modern smart cities | ||
| 336 | |a Текстовый файл | ||
| 461 | 1 | |t Smart Cities |c Basel |n MDPI AG | |
| 463 | 1 | |t Vol. 8, iss. 1 |v Article number 14, 17 p. |d 2025 | |
| 610 | 1 | |a spatial clustering | |
| 610 | 1 | |a optimisation | |
| 610 | 1 | |a geographic segmentation | |
| 610 | 1 | |a Ward’s method | |
| 610 | 1 | |a water resource management | |
| 610 | 1 | |a silhouette quotient | |
| 610 | 1 | |a infrastructure planning | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 701 | 1 | |a Kapanski |b A. A. |g Aliaksey | |
| 701 | 1 | |a Klyuev |b R. V. |g Roman Vladimirovich | |
| 701 | 1 | |a Boltrushevich |b A. E. |g Aleksandr Evgenjevich | |
| 701 | 1 | |a Sorokova |b S. N. |c specialist in the field of Informatics and computer engineering |c associate Professor of Tomsk Polytechnic University, programmer, candidate of physico-mathematical Sciences |f 1981- |g Svetlana Nikolaevna |9 16596 | |
| 701 | 1 | |a Efremenkov (Ephremenkov) |b E. A. |c Specialist in the field of mechanical engineering |c Associate Professor of Tomsk Polytechnic University, Candidate of Technical Sciences (PhD) |f 1975- |g Egor Alekseevich |9 14780 | |
| 701 | 1 | |a Demin |b A. Yu. |c specialist in the field of Informatics and computer engineering |c Associate Professor of Tomsk Polytechnic University, candidate of technical sciences |f 1973- |g Anton Yurievich |9 17327 | |
| 701 | 1 | |a Martyushev |b N. V. |c specialist in the field of material science |c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1981- |g Nikita Vladimirovich |9 16754 | |
| 801 | 0 | |a RU |b 63413507 |c 20250506 | |
| 850 | |a 63413507 | ||
| 856 | 4 | |u http://earchive.tpu.ru/handle/11683/132431 |z http://earchive.tpu.ru/handle/11683/132431 | |
| 856 | 4 | |u https://doi.org/10.3390/smartcities8010014 |z https://doi.org/10.3390/smartcities8010014 | |
| 942 | |c CF | ||