Using Machine Learning and Neural Networks Technologies, a Bottom-Up Water Process Is Being Used To Reduce All Water Pollution Diseases

Abstract

Without water, we can’t do anything, and water plays the most important role in our regular life. Actually, the initial stage of water provides excellent water quality; it’s very healthy for the human body. The water continues to flow, but gradually the water becomes polluted and affects human health. Water pollution is one of the major problems on earth nowadays. Some people do some activities; animals, underground water, and industries release wastewater, so gradually the water becomes polluted. We studied a number of papers and searched for water pollution diseases and changes in water quality and the drinking water problem, which causes almost 250 children to die each day, is one of the worldwide risks listed by the World Economic Forum. Every year, almost 4 million people die as a result of drinking contaminated water. Despite technological developments, there aren't enough quality measures available to assess drinking water quality. So, we proposed the bottom-up process of water to reduce all water pollution diseases using hybrid technologies and to decrease all water pollution problems. Machine learning has been widely employed as a potent tool to address issues in the water environment since it can be used to forecast water quality, allocate resources more effectively, handle shortages of resources, etc. We will discuss several methods of machine learning, including classification, regression, and neural network-based techniques. Using data from the last 10 years, it's easy to figure out who is polluting and hurting the water. When implement a use this hybrid technologies gradually reduce all water related issues. In future definitely we get excellent water quality and peoples, animals get healthy water.