Water Quality Prediction Using Statistical Tool and Machine Learning Algorithm

Abstract

Water is the most valuable natural resource for all living things and the ecosystem. The quality of groundwater is changed due to change in ecosystem, industrialisation, and urbanisation, etc. In the study, 60 samples were taken and analysed for various physio-chemical parameters. The sampling locations were located using global positioning system (GPS) and were taken for two consecutive years for two different seasons, monsoon (Nov-Dec) and post-monsoon (Jan-Mar). In 2016-2017 and 2017-2018 pH, EC, and TDS were obtained in the field. Hardness and Chloride are determined using titration method. Nitrate and Sulphate were determined using Spectrophotometer. Machine learning techniques were used to train the data set and to predict the unknown values. The dominant elements of groundwater are as follows: Ca2, Mg2 for cation and Cl-, SO42, NO3− for anions. The regression value for the training data set was found to be 0.90596, and for the entire network, it was found to be 0.81729. The best performance was observed as 0.0022605 at epoch 223.

AO-SVM: a machine learning model for predicting water quality in the cauvery river

“…The primary causes of water contamination in developing nations are unregulated human activities, such as chemical usage in agriculture, construction, industrialization, mining, and urbanization (Kalaivanan and Vellingiri 2023). These activities can introduce harmful chemicals and bacteria into water sources, leading to waterborne illnesses like cholera, dysentery, giardia, infectious hepatitis, nasal congestion, paratyphoid, pneumonia, schistosomiasis, and typhoid (Beerala et al 2020).…”