Classification of Water Quality Level using Deep Learning on Android Application

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Nattavadee Hongboonmee
Thiraphong Oonkham


Water is crucial for all lives, yet recently its quality is increasingly affected by waste and pollution. As water quality inspection is important for ecological and water resource management, this work therefore proposed a water quality inspection technique based on deep learning to automatically classify water quality on an Android application. The process started from collection of 480 images which were divided into four grades of water: A, B, C and D. The images were used for training a model for water quality classification, and three algorithms were compared: MobileNet 0.5 MobileNet 0.75, and MobileNet 1.0. Model assessment was done using accuracy, precision and recall. The model was developed into an application, which was then finally tested. The test found that (1) the MobileNet 1.0 was the most suitable model with 100.00% accuracy, precision and recall, and (2) application test showed that it had classification success rate of 82.50%, with good performance. Application was assessed by experts and the result was 4.33 on average, or highly suitable. The test showed that this approach and application could be used for water quality inspection from images with high efficiency and ease of use.


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How to Cite
N. Hongboonmee and T. Oonkham, “Classification of Water Quality Level using Deep Learning on Android Application”, JIST, vol. 12, no. 2, pp. 31–41, Dec. 2022.
Research Article: Information Systems (Detail in Scope of Journal)


N. Tangkhananurak and K. Tangkhananurak, Principles of chemical water quality analysis. Bangkok: Kasetsart University Press, 2007.

Department of Health, “Water quality criteria,” Department of Health Laboratory Center, 2021. [Online]. Available:https:// [Accessed: Aug. 9, 2021].

Department of National Parks Wildlife and Plant Conservation, “Water quality of the basin,” Department of National Parks Wildlife and Plant Conservation, 2021. [Online]. Available: [Accessed: Sep. 19, 2021].

Pollution Control Department, “Water quality and water pollution,” Pollution Control Department, 2021.[Online]. Available: [Accessed: Sep. 12, 2021].

K. Sraubon, LearnAI:Deep Learning with Python. Bangkok: Intermedia Press, 2022.

N. Phromrit and S. Wichanya, Fundamental of Deep Learning in Practice. Bangkok: IDC Premier, 2021.

G. Lin, and W. Shen, “Research on Convolutional Neural Network based on Improved Relu - piecewise Activation Function,” Procedia Computer Science, vol. 131, no.1,pp. 977-984, 2018.

F. Pu, C. Ding, Z. Chao, Y. Yu, and X. Xu, “Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks,” Remote Sensing, vol. 11, no. 14, pp. 1674-1688, 2019.

A. Mahmood, M. Bennamoun, S. An, F. Sohel, F. Boussaid,R. Hovey, G. Kendrick,and R. Fisher, “Automatic annotation of coral reefs using deep learning,” In Proceedings of OCEANS 2016 MTS/IEEE Monterey, pp. 1-5, 2016.

R. Barzegar, M. Aalami and J. Adamowski, “Short-term water quality variable prediction using hybrid CNN-LSTM deep learning model,” Stochastic Environmental Research and Risk Assessment, vol. 34, no. 1, pp. 415-433, 2020.

J. Orlando, E. Prokofyeva,M. Fresno and M. Blaschko, “An ensemble deep learning based approach for red lesion detection in fundus images,” Computer Methods and Programs in Biomedicine, vol. 2018, no. 153, pp. 115-127, 2018.

S. Krishnan, S. Antani and S. Jaeger, “Visualizing Deep Learning Activations for Improved Malaria Cell Classification,”In Proceedings of First workshop Medical Informatics and Healthcare, Machine Learning Research, pp. 1-8, 2017.

H. Wu and Z. Zhou, “Using convolution neural network for defective image classification of industrial components,” Mobile Information Systems, vol. 2021, no. 1, pp. 1-8, 2021.

D. Sinha and M. El-Sharkawy, “Thin MobileNet: An Enhanced MobileNet Architecture,” In Proceedings of IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON2019), pp. 0280-0285, 2019.

T. Arfan, M. Hayaty, and A. Hadinegoro, “Classification of Brain Tumours Types based on MRI Images using Mobilenet,” In Proceedings of 2ndInternationalConference on Innovation and Creative Information Technology (ICITech2021), pp. 69-73, 2021.

S. Naseer, MR Ali, A. Muneer, and A. Fati, “iAmideV-Deep: ValineAmidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Composition,” Symmetry, vol. 2021, no. 13, pp. 1-19, 2021.