Hydroinformatics Vision 2011

Holz, Klaus-Peter; Cunge, Jean A.; Lehfeldt, Rainer; Savić, Dragan

doi:10.1007/978-981-4451-42-0_44

Cited by 5 publications

(3 citation statements)

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“…In addition to statistical or stochastic methodologies, new research approaches have been developed in the WDS field. In recent years, Hydroinformatics has integrated water sciences, data sciences, artificial intelligence, and social sciences [20]. The models proposed from data mining and machine learning have become popular in the last decade [21].…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

Granados

Zanfei

Brentan

et al. 2022

Water

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Water distribution system monitoring is currently carried out using advanced real-time control technologies to achieve a higher operational efficiency. Data analysis techniques can be implemented for condition estimation, which are crucial tools for managing, developing, and operating water networks using the monitored flow rate and pressure data at some network pipes and nodes. This work proposes a state estimation methodology that enables one to infer the hydraulic state of the operating speed of pumping systems from these pressure and flow measurements. The presented approach suggests using graph convolutional neural network theory linked to hydraulic models for generating a digital twin of the water system. It is validated on two benchmark hydraulic networks: the Patios-Villa del Rosario, Colombia, and the C-Town networks. The results show that the proposed model effectively predicts the state estimation in the two hydraulic networks used. The results of the evaluation metrics indicate low values of mean squared error and mean absolute error and high values of the coefficient of determination, reflecting high predictive ability and that the prediction results adequately represent the real data.

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Section: Introductionmentioning

confidence: 99%

A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

Granados

Zanfei

Brentan

et al. 2022

Water

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“…In order to identify which and how ICT solutions can be implemented, it is necessary to look at the water cycle through an approach based on functional domains and business processes. This methodology allows considering each action involved in resource management and identifying the potential ICT needs [11,[13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Water Distribution Strategy with Smart Water Grid

et al. 2015

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Many problems that are encountered in regards to water balance and resources management are related to challenges of economic development under limited resources and tough competition among various water uses. The development of major infrastructure like airports in remote areas that have limited water resources is becoming a common problem. In order to overcome these difficulties, water management has to articulate and combine several resources in order to respond to various demands while preserving the ecological quality of the environment. The paper discusses the interest in implementing the Smart Water Grid concept on Yeongjongdo Island, which is the location of Korea's main airport. This new concept is based on the connection of various water resources and their optimized management with new information technology solutions. The proposed system integrates water generated through rainfall, external water resources (i.e., metropolitan water distribution system), gray water and other types of alternative water resources. The paper analyses the feasibility of this approach and explores interest in the Smart Water Grid concept.

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“…Data‐driven modelling (Solomatine, ; Solomatine and Ostfeld, ; Elshorbagy et al ., ,) is a major component of hydroinformatics (Abbott, ; ; See et al ., ; Abrahart et al ., ; Holz et al ., ), in which emerging technological products, primarily related to developments in computational intelligence and machine learning algorithms, are applied to complex hydrological problems. In data‐driven modelling, the responsibility for identifying model structure is largely passed to computer algorithms, which are not constrained by a need for their solutions to conform to fundamental concepts in hydrology (Mount and Abrahart, ).…”

Section: Introductionmentioning

confidence: 99%

Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

et al. 2012

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Multi‐step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3‐h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real‐time rainfall‐runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3 h. In this paper, we develop a novel semi‐distributed, data‐driven, rainfall‐runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network‐based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point‐based rain gauge predictors. Different levels of spatially aggregated radar‐derived rainfall data are used to generate 4, 8 and 12 sub‐catchment input drivers. In general, the semi‐distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3 h. Performance is found to be optimal when spatial aggregation is restricted to four sub‐catchments, with up to 30% improvements in the performance over lumped and point‐based models being evident at 5‐h lead times. The potential benefits of applying semi‐distributed, data‐driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. Copyright © 2012 John Wiley & Sons, Ltd.

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Hydroinformatics Vision 2011

Cited by 5 publications

References 0 publications

A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

Sustainable Water Distribution Strategy with Smart Water Grid

Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

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