Autor(es): Bezerra, [UNESP] ; de Oliveira Bezerra, Alberto Guilherme [UNESP] ; Libânio, Marcelo ; Lopes, Mara Lúcia Martins
Data: 2020
Identificador Persistente: http://hdl.handle.net/11449/200599
Origem: Oasisbr
Assunto(s): Artificial neural network; SNIS, forecast models; Water tariff; Artificial neural network; Artificial neural network; SNIS, forecast models; SNIS, forecast models; Water tariff; Water tariff
Made available in DSpace on 2020-12-12T02:10:54Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-06-01
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
This paper has as a main goal to evaluate how models of the forecast will work with a group of variables that were selected based only on their correlation with the average tariff variation. Two forecast models are used, the first based on multiple linear regression techniques and the second based on the application of artificial neural networks (perceptron). We intend to use those models to reach the current water tariff based on the historic variation of the charge and the selected variables applied to municipal and private companies that operate water supply and wastewater systems in the South and Southeast regions of Brazil. The subsidiary data for the elaboration of the models were obtained through the National Sanitation Information System (SNIS). The obtained results indicated that the forecasting processes, in both models used, were able to forecast with high accuracy the fees, and guaranteed the maintenance of the surplus for the analyzed systems.
Departamento de Engenharia Civil Faculdade de Engenharia de Ilha Solteira - UNESP
Departamento de Engenharia Hidráulica e Recursos Hídricos Universidade Federal de Minas Gerais Escola de Engenharia, Av. Antônio Carlos, 6627, Bloco I, Escola de Engenharia, Sala 4610, Pampulha
Departamento de Matemática da Faculdade de Engenharia de Ilha Solteira, Avenida Brasil, n° 56 – Centro
Departamento de Engenharia Civil Faculdade de Engenharia de Ilha Solteira - UNESP
