Water Quality Classification Using an Artificial Neural Network (ANN)

Abstract

Malaysia is currently a rapidly developing country to achieve a 2020 vision. However the development that has been carried out contributed to a negative impact on the environment especially on water quality. Due to the deterioration of water quality, serious management efforts on water quality has been taken. Thus, the aim of this study is to investigate a technique that can automatically classify the water quality. The technique is based on the concept of Artificial Neural Network (ANN). Since the greater part of their methodologies depend on the idea of `pattern recognition’. Thus, it is convenient to inspect its ability in classify water quality. There are six environmental data were used in this study such as pH, total suspended solids (TSS), dissolved oxygen (DO), chemical oxygen demand (COD), biological oxygen demand (BOD), and ammonia. The data was obtained by in-site measurement and laboratory analysis. Then, the data was used as the feeder of input variables in the ANN database system. After training and testing the network of ANN, the result showed that 80.0% of accuracy classification with 0.468 of root mean square error (RMSE). This showed the encouraging results for classification.

The Use of Artificial Neural Networks to Predict the Physicochemical Characteristics of Water Quality in Three District Municipalities, Eastern Cape Province, South Africa

“…These results agree with the findings of the present study ( Table 3 ). Several other studies are consistent with the present study and conclude, based on similar findings, that the ANN model can easily classify and predict water quality with the justifiable output [ 19 , 20 , 50 , 55 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ].…”

“…Owing to an increase in data scale and the growing need to investigate ways and means of linking together land use, pollutant loading and disposal, water quality, and ecosystem impacts, mathematical techniques and models that can efficiently model and predict water quality have been developed [ 19 ]. These modeling techniques can systematically and methodically understand the cause-and-effect relationships and assess water quality changes [ 20 ]. This ability is crucial to forecasting the variation trend of water quality at a particular time in the future [ 21 ].…”

Assessment of sustainability index for rural water management using ANN

“…Application of ANN in the municipal water network of Canadian municipalities benefitted in the performance assessment and prediction of the prevailing water networks' rehabilitation (Al-Barqawi & Zayed 2008). Another literature study infers that an ANN model assisted to analyse and improve the water quality standards in the West Coast of Malaysia (Sulaiman et al 2019). Hence, previous studies suggest SI's determination concerning the concurrent conditions, whereas this proposed research generates SI for future water management scenarios.…”

Recognizing Safe Drinking Water and Predicting Water Quality Index using Machine Learning Framework

“…It consists of two subsystems; the first subsystem is responsible for classifying water quality based on nine AI models that have been applied, tested, and compared to classify various samples of drinking water as safe to drink or unsafe to drink. The applied nine AI www.ijacsa.thesai.org models are: Extreme Gradient Boosting (XGBoost) [10], Light Gradient Boosting Machine (Light GBM) [11], Decision Tree (DT) [12], Extra Tree (ET) [13], Multi-layer Perceptron (MLP) [14], Gradient Boosting (GB) [15], Support Vector Machine (SVM) [16], Artificial Neural Network (ANN) classification [17], and Random Forest (RF) Classifier [18]. The second subsystem is responsible for predicting water quality index (WQI) based on six regression models, LGBM regression, XGB regression, ExtraTrees regression, DT Regression, RF regression, and linear regression.…”