Science in Information Technology Letters
Vol 2, No 2: November 2021

Water quality identification based on remote sensing image in industrial waste disposal using convolutional neural networks

Widiharso, Prasetya (Unknown)
Handoko, Wahyu Tri (Unknown)
Wibawa, Aji Prasetya (Unknown)
Handayani, Anik Nur (Unknown)
Teng, Ming Foey (Unknown)



Article Info

Publish Date
30 Nov 2021

Abstract

Measuring the quality of river water used as industrial wastewater disposal is needed to maintain water quality from pollution. The chemical industry produces hazardous waste containing toxic materials and heavy metals. At specific concentrations, industrial waste can result in bacteriological contamination and excessive nutrient load (eutrophication). Using the Convolutional Neural Network (CNN), the method for measuring water quality processes remote sensing images taken via an RGB camera on an Unmanned Aerial Vehicle (UAV). The parameter measured is the change in the color of the river water image caused by the chemical reaction of the heavy metal content of industrial waste disposal. The test results of the Convolutional Neural Network (CNN) method in 2.01s/step obtained the value of training loss mode 17.86%, training accuracy 90.62%, validation loss 23.43%, validation accuracy 83.33%.

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Journal Info

Abbrev

sitech

Publisher

Subject

Computer Science & IT

Description

Science in Information Technology Letters (SITech) aims to keep abreast of the current development and innovation in the area of Science in Information Technology as well as providing an engaging platform for scientists and engineers throughout the world to share research results in related ...