Predict the Quality of Freshwater using Support Vector Machines

Abstract

The purity of the water has recently been threatened by a number of contaminants. As a result, it is now crucial for the management of water pollution to model and anticipate water quality. In order to forecast the water quality index (WQI) and water quality classification (WQC), this work creates cutting-edge artificial intelligence (AI) approaches. Today, many people are afflicted with severe illnesses brought on by tainted water. This study will look at a water quality monitoring system because it provides information on water quality. It is planned to identify forecasts for water quality using a machine learning system. The depletion of natural water resources including lakes, streams, and estuaries is one of the most significant and alarming issues facing humanity. The effects of dirty water are widespread and have an impact on several people. Water resource management is therefore essential for maximizing water quality. If data are analyzed and water quality is foreseen, the effects of water contamination can be effectively addressed. Even though this subject has been covered in a large number of earlier research, more has to be done to boost the effectiveness, dependability, accuracy, and utility of the current techniques to managing water quality. The goal of this study is to develop an Artificial Neural Network (ANN) and time-series analysisbased water quality prediction model. The historical water quality data used in this study has a 6-minute time period and is from the year 2014. The National Water Information System, a website operated by the United States Geological Survey (USGS) is where the data comes from.