NDLI: Water Demand Forecasting Model for the Metropolitan Area of São Paulo, Brazil

Content Provider	Springer Nature Link
Author	Santos, Cláudia Cristina Pereira Filho, Augusto José
Copyright Year	2014
Abstract	This work is concerned with forecasting water demand in the metropolitan area of São Paulo (MASP) through water consumption, meteorological and socio-environmental variables using an Artificial Neural Network (ANN) system. Possible socio-environmental and meteorological conditions affecting water consumption at Cantareira water treatment station (WTS) in the MASP, Brazil were analyzed for the year 2005. Eight model configurations were developed and used for the Cantareira WTS. The best performance was obtained for 12-h average of the input variables. The ANN model performed best with three times steps in advance. The hourly forecasting was obtained with acceptable error levels. Model results indicate an overall tendency for small errors. The proposed method is useful tool for water demand forecasting and water systems management. The paper is an important contribution since it takes into account weather variables and introduces some diagnostic studies on water consumption in one of the largest urban environments of the planet with its unique peculiarities such as anthropic affects on weather and climate that feeds back into the water consumption. The averaging is a low pass filter indeed and we used it to improve Signal to Noise Ratio (SNR).
Starting Page	4401
Ending Page	4414
Page Count	14
File Format	PDF
ISSN	09204741
Journal	Water Resources Management
Volume Number	28
Issue Number	13
e-ISSN	15731650
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-08-09
Publisher Institution	European Water Resources Association (EWRA)
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Water consumption Forecasting Artificial neural network Water supply system Urban system Water resources Hydrogeology Hydrology/Water Resources Geotechnical Engineering & Applied Earth Sciences Atmospheric Sciences Civil Engineering Environment
Content Type	Text
Resource Type	Article
Subject	Water Science and Technology Civil and Structural Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models

Spatial Determinants of Urban Residential Water Demand in Fortaleza, Brazil

Probabilistic Water Demand Forecasting Using Projected Climatic Data for Blue Mountains Water Supply System in Australia

Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks

Evaluating Municipal Water Management Options with the Incorporation of Water Quality and Energy Consumption

Bridging the Water Supply–demand Gap in Australia: Coupling Water Demand Efficiency with Rain-independent Desalination Supply

Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites

Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series

Using System Dynamics Method to Determine the Effect of Water Demand Priorities on Downstream Flow

Water Demand Forecasting Model for the Metropolitan Area of São Paulo, Brazil

Similar Documents

Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models

Spatial Determinants of Urban Residential Water Demand in Fortaleza, Brazil

Probabilistic Water Demand Forecasting Using Projected Climatic Data for Blue Mountains Water Supply System in Australia

Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks

Evaluating Municipal Water Management Options with the Incorporation of Water Quality and Energy Consumption

Bridging the Water Supply–demand Gap in Australia: Coupling Water Demand Efficiency with Rain-independent Desalination Supply

Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites

Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series

Using System Dynamics Method to Determine the Effect of Water Demand Priorities on Downstream Flow

Water Demand Forecasting Model for the Metropolitan Area of São Paulo, Brazil