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Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study

Acceso Abierto
ID Minciencias: ART-0001249398-196
Ranking: ART-GC_ART

Abstract:

Abstract This paper presents an application of an artificial neural network model in forecasting urban water demand using MATLAB software. Considering that in any planning process, the demand forecast plays a fundamental role, being one of the premises to organize and control a set of activities or processes. The versatility of the short, medium and long-term prediction that is provided to the company that offers the water distribution service to determine the supply capacity, maintenance activities, and system improvements as a strategic planning tool. Shown to improve network performance by using time series water demand data, the model can provide excellent fit and forecast without relying on the explicit inclusion of climatic factors and number of consumers. The excellent accuracy of the model indicates the effectiveness of forecasting over different time horizons. Finally, the results obtained from the Artificial Neural Network are compared with traditional statistical models.

Tópico:

Hydrological Forecasting Using AI

Citaciones:

Citations: 10
10

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Información de la Fuente:

SCImago Journal & Country Rank
FuenteJournal of Physics Conference Series
Cuartil año de publicaciónNo disponible
Volumen1284
Issue1
Páginas012004 - 012004
pISSNNo disponible
ISSN1742-6596

Enlaces e Identificadores:

Publicaciones editoriales no especializadas