MACHINE LEARNING PREDICTS FRESHWATER QUALITY MEASUREMENTS

MACHINE LEARNING PREDICTS FRESHWATER QUALITY MEASUREMENTS

K.Kirubananthavalli, Mr.S. Prasanth
Assistant Professor /CSE,
Unnamalai  Institute of Technology, Kovilpatti.
Tamil Nadu,India

Abstract - Multiple pollutants have lately emerged as a concern to the water supply. Therefore, modelling and anticipating water quality has become essential for the control of water pollution. In order to better predict the WQI and WQC, this study builds state-of-the-art artificial intelligence (AI) algorithms. Many individuals nowadays suffer from life-threatening diseases because of drinking contaminated water. Our research focuses on a water quality monitoring system because of the information it provides. We are preparing to use a machine learning system to determine water quality predictions. One of the most serious and disturbing problems society is now experiencing is the loss of natural water resources such as lakes, streams, and estuaries. Polluted water has far-reaching consequences that affect many individuals. As a result, optimising water quality requires careful management of available water resources. The impacts of water pollution may be successfully handled if data are assessed and water quality is predicted. While much has been written on the topic of water quality management, more work has to be done to improve the present methods' efficacy, reliability, accuracy, and usefulness. The purpose of this research is to create a model for predicting water quality using Artificial Neural Networks (ANNs) and time-series analysis. The 6-minute time period 2014 historical water quality data utilised in this investigation. The information was collected by the USGS and is hosted on their National Water Information System (NWIS) website.

Keywords: water quality, machine learning, prediction and modelling, predictive algorithms, water resources.