Image-Based River Water Level Estimation for Redundancy Information Using Deep Neural Network

Gabriela Rocha de Oliveira Fleury, Douglas Vieira do Nascimento, Arlindo Rodrigues Galvão Filho, Filipe de Souza Lima Ribeiro, Rafael Viana de Carvalho and Clarimar José Coelho
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Gabriela Rocha de Oliveira Fleury: Scientific Computing Lab, Pontifical Catholic University of Goiás, Goiânia 74175-720, GO, Brazil
Douglas Vieira do Nascimento: Scientific Computing Lab, Pontifical Catholic University of Goiás, Goiânia 74175-720, GO, Brazil
Arlindo Rodrigues Galvão Filho: Scientific Computing Lab, Pontifical Catholic University of Goiás, Goiânia 74175-720, GO, Brazil
Filipe de Souza Lima Ribeiro: Jirau Hidroeletric Power Plant, Energia Sustentável do Brasil, Porto Velho 76840-000, RO, Brazil
Rafael Viana de Carvalho: Scientific Computing Lab, Pontifical Catholic University of Goiás, Goiânia 74175-720, GO, Brazil
Clarimar José Coelho: Scientific Computing Lab, Pontifical Catholic University of Goiás, Goiânia 74175-720, GO, Brazil

Energies, 2020, vol. 13, issue 24, 1-12

Abstract: Monitoring and management of water levels has become an essential task in obtaining hydroelectric power. Activities such as water resources planning, supply basin management and flood forecasting are mediated and defined through its monitoring. Measurements, performed by sensors installed on the river facilities, are used for precisely information about water level estimations. Since weather conditions influence the results obtained by these sensors, it is necessary to have redundant approaches in order to maintain the high accuracy of the measured values. Staff gauge monitored by conventional cameras is a common redundancy method to keep track of the measurements. However, this method has low accuracy and is not reliable once it is monitored by human eyes. This work proposes to automate this process by using image processing methods of the staff gauge to measure and deep neural network to estimate the water level. To that end, three models of neural networks were compared: the residual networks (ResNet50), a MobileNetV2 and a proposed model of convolutional neural network (CNN). The results showed that ResNet50 and MobileNetV2 present inferior results compared to the proposed CNN.

Keywords: hydroeletric power plant; water level; redundancy information; CNN; residual networks; MobileNetV2 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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