Zobrazeno 1 - 10
of 301
pro vyhledávání: '"Water demand forecasting"'
Publikováno v:
Water Research X, Vol 25, Iss , Pp 100269- (2024)
Short-term water demand forecasting (STWDF) for multiple spatially and temporally correlated District Metering Areas (DMAs) is an essential foundation for achieving more refined management of urban water supply networks. However, due to the greater u
Externí odkaz:
https://doaj.org/article/d869b2e445eb443a843109e37f293f20
Autor:
Jesuino Vieira Filho, Arlan Scortegagna, Amanara Potykytã de Sousa Dias Vieira, Pablo Andretta Jaskowiak
Publikováno v:
Water Practice and Technology, Vol 19, Iss 5, Pp 1586-1602 (2024)
Water resources management is crucial for human well-being and contemporary socio-economic development. However, the increasing use of water has led to various problems that affect its quality and availability. To address these issues, accurate forec
Externí odkaz:
https://doaj.org/article/d018fb58a6f9430bbf95f270614543a1
Publikováno v:
Water Research X, Vol 24, Iss , Pp 100247- (2024)
In the field of water supply management, multi-steps water demand forecasting plays a crucial role. While there have been many studies related to multi-steps water demand forecasting based on deep learning, little attention has been paid to the inter
Externí odkaz:
https://doaj.org/article/967eba6f7f614f5daa93832979280452
Autor:
Lin, Peijie a, Zhang, Xiangxin a, Gong, Longcong b, Lin, Jingwei b, Zhang, Jie a, ⁎, Cheng, Shuying a, ⁎
Publikováno v:
In Journal of Hydrology April 2025 651
Publikováno v:
Water Supply, Vol 24, Iss 4, Pp 1352-1363 (2024)
Water management is a major contemporary and future challenge. In an increasing water demand scenario related to climate change, a water distribution system must ensure equal access to water for all users. In this context, a reliable short-term water
Externí odkaz:
https://doaj.org/article/57302e5d0aba4f6282e35b8773b1f730
Autor:
Zhi-Wei Tian, Ru-Liang Qian
Publikováno v:
IEEE Access, Vol 12, Pp 115853-115867 (2024)
This paper presents a novel deep learning-based model for forecasting water demand Specifically, a transformer network architecture-based iTransformer model is introduced to forecast total water demand at both country and province levels over the med
Externí odkaz:
https://doaj.org/article/24e5359eb05c471c831e8b48f7fe2427
Publikováno v:
Information, Vol 15, Iss 10, p 605 (2024)
This study presents an improved data-centric approach to short-term water demand forecasting using univariate time series from water reservoir levels. The dataset comprises water level recordings from 21 reservoirs in Eastern Thessaloniki collected o
Externí odkaz:
https://doaj.org/article/b791c9729e22432ebd3671aa91cf2a39
Publikováno v:
Journal of Hydroinformatics, Vol 25, Iss 3, Pp 895-911 (2023)
Accurate water demand forecasting is the key to urban water management and can alleviate system pressure brought by urbanisation, water scarcity and climate change. However, existing research on water demand forecasting using machine learning is focu
Externí odkaz:
https://doaj.org/article/823eadcec10142159ae89b5e6b41888e
Autor:
Hesam Kamyab, Tayebeh Khademi, Shreeshivadasan Chelliapan, Morteza SaberiKamarposhti, Shahabaldin Rezania, Mohammad Yusuf, Mohammad Farajnezhad, Mohamed Abbas, Byong Hun Jeon, Yongtae Ahn
Publikováno v:
Results in Engineering, Vol 20, Iss , Pp 101566- (2023)
The effective management of water resources is essential to environmental stewardship and sustainable development. Traditional approaches to water resource management (WRM) struggle with real-time data acquisition, effective data analysis, and intell
Externí odkaz:
https://doaj.org/article/146bebfc27af49559411924828bdba15
Publikováno v:
Water Supply, Vol 23, Iss 2, Pp 624-644 (2023)
The specialized literature on water demand forecasting indicates that successful predicting models are based on soft computing approaches such as neural networks, fuzzy systems, evolutionary computing, support vector machines and hybrid models. Howev
Externí odkaz:
https://doaj.org/article/c5e27e0f1c5f49dfbd5647fa66e6cbe6