The Use of Neural Network Coupled with Image Processing for Water Quality Assessment (Location: Hot Spring Mae-Khachan, Thailand)

Wahyu Nurkholis Hadi Syahputra, Chatchawan Chaichana, Wasin Wingwilai, Braja Manggala


Parameters such as pH, alkalinity, total hardness, and nitrate are important to determine the quality of agricultural purpose. Water quality testing strips are popular for testing the water. In addition to the low price, this tool is very easy to use. However, bias due to colour reading from different users exists. In this study, the evaluation of water quality with image processing coupled with the artificial neural network (ANN) model is proposed. Water quality is assessed using testing strips. Images of the strip are taken by mobile-phones and geo-tag provided by GPS. Then, colour-space transformation, image enhancement, and regions of interest (ROI) are carried out as part of the image processing stage. Finally, the ANN-multi layer perceptron (MLP) is used to obtain water quality predictions based on the results of image analysis. Based on the results of the analysis, the R2 values > 0.80 for the estimating water parameters. This research shows that the image processing coupled with ANN has the potential to get a more precise estimation value for water quality assessment. For future work, the geo-tagging application meets with the data centre system in this study offers periodic monitoring of water quality in a large area.


Artificial neural network; Environment monitoring; Image processing; Low-cost instrument; Water quality

Full Text:



Kılıç Z., 2020. The importance of water and conscious use of water. International Journal of Hydrology 4(5): 239–241.

Liu Y., Hu X., Zhang Q., and Zheng M., 2017. Improving agricultural water use efficiency: A quantitative study of Zhangye City using the static CGE model with a ces water-land resources account. Sustainability 9(2).

Mandal S.K., Dutta S.K., Pramanik S., and Kole R.K., 2019. Assessment of river water quality for agricultural irrigation. International Journal Environmental Science and Technology 16(1): 451–462,.

Mzini L.L. and K. Winter. 2015. Effects of irrigation water quality on vegetables Part 1: Yield and aesthetical appeal, South African Journal of Plant and Soil 32(1): 27–31.

Teprek A., Poetri Artono V., Waiyawat W., Limsakul A., Shiowatana J., and Siripinyanond A., 2020. Semi-quantitative analysis by spot counting on origami paper-based device for endpoint detection in titrimetric analysis. Microchemical Journal 158: 105284.

Shariati-Rad M. and S. Heidari. 2020. Classification and determination of total hardness of water using silver nanoparticles. Talanta 219: 121297.

Gauthama B.U., Narayana B., Sarojini B.K., Bello K., and Suresh N.K., 2020. Nitrate/Nitrite determination in water and soil samples accompanied by in situ azo dye formation and its removal by superabsorbent cellulose hydrogel, SN Applied Science 2(7).

Oelen A., Van Aart C., and De Boer V., 2018. Measuring surface water quality using a low-cost sensor kit within the context of rural Africa. 5th International Symposium "Perspectives on ICT4D", P-ICT4D 2018, Amsterdam, Netherlands, May 27. CEUR Workshop Proceedings.

Flores-Anderson A.I., Griffin R., Dix M., Romero-Oliva C.S., Ochaeta G., Skinner-Alvarado J., Ramirez Moran M.V., Hernandez B., Cherrington E., Page B., and Barreno F., 2020. Hyperspectral satellite remote sensing of water quality in Lake Atitlán, Guatemala. Frontiers in Environmental Science 8.

Singha S.S., Devatha C.P, Singha S., and Verma M.K., 2015. Assessing ground water quality using GIS. International Journal of Engineering Research and Technology 4(11): 689–694.

Sibanda M., Mutanga O., Chimonyo V.G.P., Clulow A.D., Shoko C., Mazvimavi D., Dube T., and Mabhaudhi T., 2021. Application of drone technologies in surface water resources monitoring and assessment: A systematic review of progress, challenges, and opportunities in the global south, Drones MDPI 5(3): 1–21.

Lakshmikantha V., Hiriyannagowda A., Manjunath A., Patted A., Basavaiah J., and Anthony A.A., 2021. IoT based smart water quality monitoring system, Global Transitions Proceedings 2(2): 181–186.

Horak K., Klecka J., and Richter M., 2015. Water quality assessment by image processing. In 2015 38th International conference Telecommunications and Signal Processing, TSP 2015. Prague, Czech Republic, 09-11 July IEEE.

Putra B.T.W., Purwoko R.S., Indarto I., and Soni P., 2019. An investigation of copper chlorophyllin solution for low-cost optical devices calibration in chlorophyll measurement. International Journal of Metrology and Quality Engineering 10.

Najah Ahmed A., Binti Othman F., Abdulmohsin Afan H., Khaleel Ibrahim R., Ming Fai C., Shabbir Hossain M., Ehteram M., and Elshafie A., 2019. Machine learning methods for better water quality prediction. Journal of Hydrology 578: 124084.

Jeong J.Y., Kang J.S., and Jun C.H., 2020. Regularization-based model tree for multi-output regression. Information Sciences 507: 240–255.

Putra B.T.W., Wirayuda H.C., Syahputra W.N.H., and Prastowo E., 2021. Evaluating in-situ maize chlorophyll content using an external optical sensing system coupled with conventional statistics and deep neural networks. Measurement: Journal of the International Measurement Confederation 189: 109420.

Hema D. and D.S. Kannan. 2019. Interactive color image segmentation using HSV color space. Science and Technology Journal 7(1): 37–41.

Putra B.T.W., Syahputra W.N.H., Rusdiamin, Indarto, Anam K., Darmawan T., and Marhaenanto B., 2021. Comprehensive measurement and evaluation of modern paddy cultivation with a hydroganics system under different nutrient regimes using WSN and ground-based remote sensing. Measurement: Journal of the International Measurement Confederation 178: 109420.

Ghilamicael A.M., Boga H.I., Anami S.E., Mehari T., and Budambula N.L.M., 2017. Physical and chemical characteristics of five hot springs in Eritrea. Journal of Natural Sciences Research 7(12): 88–94.

Suarez D.L., 2011. Irrigation water quality assessments. In Agricultural Salinity Assessment and Management: Second Edition. Americal Society of Civil Engineeris (ASCE): 343–370.

Boyd C.E., Tucker C.S., and Viriyatum R., 2011. Interpretation of pH, acidity, and alkalinity in aquaculture and fisheries. North American Journal of Aquaculture 73(4): 403–408.